Future hydroclimatology of the Mekong River basin simulated using ...

HYDROLOGICAL PROCESSES Hydrol. Process. 22, 1382– 1394 (2008) Published online 31 March 2008 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/hyp.6947

Future hydroclimatology of the Mekong River basin simulated using the high-resolution Japan Meteorological Agency (JMA) AGCM Anthony S. Kiem,1 * Hiroshi Ishidaira,2 Hapuarachchige P. Hapuarachchi,3 Maichun C. Zhou,2 Yukiko Hirabayashi2 and Kuniyoshi Takeuchi2,3 1 Sinclair Knight Merz, 590 Orrong Road, Armadale, Victoria 3143, Australia Takeuchi-Ishidaira Lab, Room 103, Department of Civil and Environmental Engineering, University of Yamanashi, Takeda 4-3-11, 400-8511 Kofu City, Japan International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM), Public Works Research Institute, 1-6, Minamihara, Tsukuba, Ibaraki-Ken 305-8516, Japan

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Abstract: Analysis of future Japan Meteorological Agency atmospheric general circulation model (JMA AGCM) based climate scenarios for the Mekong River basin (MRB) indicates that annual mean precipitation will increase in the 21st century (2080–2099) by 4Ð2% averaged across the basin, with the majority of this increase occurring over the northern MRB (i.e. China). Annual mean temperatures are also projected to increase by approximately 2Ð6 ° C (averaged across the MRB). As expected, these changes also lead to significant changes in the hydrology of the MRB. All MRB subbasins will experience an increase in the number of wet days in the ‘future’ and, importantly for sustainable water resources management and the mitigation of extreme events (e.g. floods and droughts), the magnitude and frequency of what are now considered extreme events are also expected to increase resulting in increased risk of flooding, but a reduction in the likelihood of droughts/low-flow periods—assuming water extraction is kept at a sustainable level. Despite the fact that the climate change impact projections are associated with significant uncertainty, it is important to act now and put in place policies, infrastructure and mitigation strategies to protect against the increased flooding that could occur. In addition, despite this study indicating a decrease in the number of ‘low-flow’ days, across most of the MRB, further analysis is needed to determine whether the reduction in low-flow days is enough to compensate for (and sustain) the rapidly increasing population and development in the MRB. Copyright  2008 John Wiley & Sons, Ltd. KEY WORDS

Yamanashi distributed hydrological model (YHyM); daily scaling; water resources management; flood; drought; sustainable development; mitigation

Received 14 February 2006; Accepted 26 March 2007

INTRODUCTION In the third assessment report (TAR) of the Intergovernmental Panel on Climate Change (IPCC, 2001), it is projected that climate change will cause increases in areaaveraged annual mean temperature over land regions of Asia during the 21st century. Precipitation is also projected to increase across Asia, particularly during the northern hemisphere winter (December to January) over Boreal Asia (the Asian land mass east of 60 ° E and north of 55° N) and over the Tibetan Plateau (China), which is the source of the Mekong River. In addition to increases in temperature and precipitation, monsoon circulation and the magnitude and frequency of extreme events are also projected to increase in the Tropical Asia region, where the Mekong River basin (MRB) is located (IPCC, 2001). Obviously the changes, if/when they occur, will have a significant influence on the hydroclimatology and water resources of the Asian region as well as the regions’ * Correspondence to: Anthony S. Kiem, Sinclair Knight Merz, 590 Orrong Road, Armadale, Victoria 3143, Australia. E-mail: [email protected] Copyright  2008 John Wiley & Sons, Ltd.

ecosystems, agriculture, forests, fisheries, domestic and industrial development, etc. This study is particularly focussed on the impact that projected climate change will have on the hydroclimatology and water resources of the MRB section of the Asian region (Figure 1). The MRB is a rapidly developing region (both in terms of population and industrial growth) that is also prone to severe and regular flooding and drought. A good understanding of future hydroclimatological conditions in the MRB is essential in order to enable more efficient water resources management, sustainable agricultural production and also to prepare now for possible changes in the frequency and magnitude of extreme climate events such as floods, droughts and bushfires. Understanding of the impacts of climate change on hydroclimatological conditions is useful everywhere, but particularly for the people living in the MRB who: – live in countries classified by the World Bank as ‘less developed’ or ‘developing’ and therefore do not currently have the same level of protection against climate extremes, which infrastructure (e.g. reservoirs and flood levees) and policies (e.g. financial assistance

FUTURE HYDROCLIMATOLOGY IN THE MEKONG SIMULATED USING THE JMA AGCM

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Figure 1. Location and digital elevation map (DEM) of the Mekong River basin

to farmers during drought) of developed countries provide; – mostly live on river plains or in cities that regularly flood (Takeuchi, 2001); – are heavily reliant on agriculture (i.e. precipitation). The majority of MRB’s economically active population are engaged in agriculture, the bulk of which is focussed on water-intensive crops such as rice and cotton. In order to gain insights into the future hydroclimatology of the MRB, this study utilizes output from a high-resolution (0Ð1875° ð 0Ð1875° or ¾20 ð ¾20 km grid size) atmospheric general circulation model (AGCM) developed by the Japan Meteorological Agency (JMA) to determine the change between ‘current’ (1979–1998) and ‘future’ (2080–2099) precipitation and temperature in the MRB. The grid-based University of Yamanashi distributed hydrological model (YHyM; Takeuchi et al., 1999; Ao et al., 2003a,b; Hapuarachchi et al., 2004a,b, 2008; Zhou et al., 2006) is then used in conjunction with the JMA AGCM output to simulate MRB’s hydrological conditions under a future (2080–2099) climate scenario. In the hydrological simulation, the spatial resolution of grid cells is 2 min (0Ð0333° ð 0Ð0333° ) and the temporal resolution is 24 h. The simulated (i.e. possible) impacts of climate change on MRB’s hydroclimatology, and opportunities for utilizing these insights to improve water resources management, are then discussed. The limitations and uncertainty associated with the methods used in this study, and avenues for future research, are also considered.

4900 km since the exact length is uncertain, due to the existence of several inaccessible tributaries at the river’s source). In terms of average annual flow, the Mekong is the 10th largest in the world and carries approximately 475 000 million cubic meters of water to the sea per year. The total area of the MRB is approximately 795 000 km2 (21st largest in the world) and it incorporates six different countries: China and Myanmar in the upper MRB and Thailand, Laos, Cambodia and Vietnam in the lower Mekong basin (Figure 1). Figure 1 also shows the digital elevation map (DEM) of the MRB where it can be seen that the Mekong’s source in the Tibetan Plateau (China) is above 4000 m but the majority of the basin is below 2700 m. Approximately 60 million people live within the MRB, while the MRB intersected countries are home to more than 240 million people. The majority of these people use water from the MRB, either directly or indirectly, for such things as irrigation, fisheries, power generation, transportation, industrial and domestic supply, etc. In much of the basin, population and development is growing rapidly which is increasing the competition for water and seeing the emergence of several water quality issues (e.g. sedimentation, salinity, eutrophication). Even if precipitation and streamflow levels remain as they are, these problems appear likely to be exacerbated given that by 2025 the population of the lower MRB (Thailand, Laos, Cambodia and Vietnam) alone is expected to reach 90 million (MRC, 2003).

DATA STUDY AREA The Mekong River is the 12th longest river in the world and the longest and most important in Southeast Asia (estimates of the Mekong’s length vary between 4100 and Copyright  2008 John Wiley & Sons, Ltd.

The JMA AGCM was used to obtain simulated daily average precipitation and daily maximum and minimum surface temperature under ‘current’ and ‘future’ climate conditions. The JMA AGCM simulations were performed using triangular truncation at wave number 959 with Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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linear Gaussian grid (TL959) in the horizontal. The transform grid uses 1920 ð 960 grid cells, corresponding to a grid size of 0Ð1875° ð 0Ð1875° (or ¾20 ð ¾20 km grid size). The model has 60 layers in the vertical side with the top of the model at 0Ð1 hPa. For further details on the JMA AGCM see Mizuta et al. (2005). In this study, 20 year ‘time-slices’ from the JMA AGCM were used to represent ‘current’ (1979–1998) and ‘future’ (2080–2099) climate conditions. For the ‘current’ simulation, boundary conditions were set based on Atmospheric Model Intercomparison Project (AMIP) II greenhouse gas concentrations and observed monthly mean climatological (November 1981 to December 1993) sea surface temperature (SST) and sea ice concentration (Reynolds and Smith, 1994). For the ‘future’ scenario simulation, greenhouse gas concentrations for the year 2090, as indicated by the IPCC Special Report on Emissions Scenarios (SRES) A1B scenario (SRES, 2000), were assigned to the model. SRES A1B represents a ‘medium’ emission scenario projection where it is assumed that a balance between fossil and nonfossil energy sources can be struck and that similar improvement rates apply to all energy supply and enduse technologies. ‘Future’ SST was obtained by adding observed 1982–1993 climatological SST used in the ‘current’ simulation to the change in SST between the average 1979–1998 and 2080–2099 SSTs obtained from the MRI-CGCM2Ð3 (Yukimoto et al., 2001; Yukimoto and Noda, 2002) run using the SRES A1B scenario. Topography in the JMA AGCM was obtained from GTOPO30 (http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html) and the land–sea distribution was determined by referring to the Global Land Cover Characteristics (GLCC) database that is compiled by the United States Geological Survey (USGS). Vegetation in the JMA AGCM is obtained from Dorman and Sellers (1989). Observed precipitation from 65 stations with complete daily data from 1972 to 2000 was obtained from the Mekong River Commission and the China Meteorological Administration. Figure 2 shows the spatial distribution of the observed precipitation stations and the number of precipitation stations within each (i) country that is intersected by the MRB and (ii) MRB subbasin. Observed daily discharge at Pakse (also shown in Figure 2) was also obtained from 1972 to 2000. Observed daily temperature was available only for the 24 stations in China. The lack of observed temperature data further south does not affect this study, as YHyM requires temperature data only to calculate snow accumulation, snow melt and soil freezing—all of which occur only in the northern most section of the MRB. To confirm negative temperatures do not occur in the lower MRB, and to validate the JMA AGCM temperature output, daily mean temperature from the CRU TS 2Ð1 (Mitchell and Jones, 2005) dataset is used. Copyright  2008 John Wiley & Sons, Ltd.

Figure 2. Number of precipitation stations within each (a) country that is intersected by the MRB and (b) MRB subbasin. The location of the Pakse discharge station is also shown

METHODOLOGY AND RESULTS Comparison between observed and JMA AGCM simulated annual average precipitation and temperature (1979–1998) The accuracy of the JMA AGCM precipitation simulations is assessed by comparing the annual mean (1979–1998) calculated from JMA AGCM daily average precipitation and the annual mean calculated from observed station data that has been transformed to 2 min (0Ð0333° ð 0Ð0333° ) grids using inverse distance weighted interpolation (Figure 3). Similarly, annual mean temperature (1979–1998) is calculated using JMA AGCM daily average temperature (i.e. the average of the daily maximum and minimum) and compared with annual mean (1979–1998) CRU TS 2Ð1 data (Figure 4). Figures 3 and 4 show that at the annual scale the JMA AGCM adequately simulates precipitation and temperature in the MRB. In Figure 3c, values greater than one (blue/green colours) indicate that JMA AGCM overestimates observed annual average precipitation. The mean ratio of JMA AGCM annual average precipitation over observed across the whole MRB is 8Ð7%, implying that the JMA AGCM has a tendency to overestimate observed annual average MRB precipitation. In Figure 4c, values greater than zero (red/yellow colours) indicate that JMA AGCM overestimates observed annual average temperature. The mean difference between JMA AGCM annual average temperature and CRU TS 2Ð1 across the whole MRB is 1Ð8 ° C, implying that the JMA AGCM has a tendency to underestimate observed annual average MRB temperature. Comparison between ‘current’ (1979–1998) and ‘future’ (2080–2099) annual average precipitation and temperature Future change in MRB precipitation and temperature is assessed by comparing the annual means calculated using the JMA AGCM daily average precipitation and temperature output for the ‘current’ and ‘future’ simulations. The Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp


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Figure 3. Annual mean precipitation from 1979 to 1998: (a) transformed from observed point data to 2 min (0Ð0333° ð 0Ð0333° ) grids using inverse distance weighted interpolation and (b) calculated using JMA AGCM daily average precipitation. (c) The ratio of (b) over (a) for each 2 min (0Ð0333° ð 0Ð0333° ) grid

Figure 4. Annual mean temperature from 1979 to 1998 calculated using (a) CRU TS 2Ð1 daily average temperature (0Ð5° ð 0Ð5° grids) and (b) the average of the daily maximum and minimum surface temperature from the JMA AGCM (0Ð1875° ð 0Ð1875° grids). (c) The difference between (b) and (a) for each 0Ð1875° ð 0Ð1875° grid

change in precipitation is shown in Figure 5c as a ratio and the change in temperature is shown in Figure 6c as the difference between ‘future’ and ‘current’. In Figure 5c, values greater than one (blue/green colours) indicate that annual average precipitation in the ‘future’ will be higher than the ‘current’ annual average. From Figure 5c, it can be seen that over the whole MRB a 4Ð2% increase in annual average precipitation is projected under the ‘future’ JMA AGCM scenario, with the majority of this increase occurring over the northern MRB (i.e. China). In Figure 6c, values greater than zero (red/yellow colours) indicate that annual average temperature in the ‘future’ will be higher than the ‘current’ annual average. From Figure 6c, it can be seen that over the whole MRB a 2Ð6 ° C increase in annual Copyright  2008 John Wiley & Sons, Ltd.

average temperature is projected under the ‘future’ JMA AGCM scenario. Problems associated with using ‘raw’ GCM output as input for YHyM Figures 3 and 4 demonstrated that annual average MRB precipitation and temperature are simulated reasonably well by the JMA AGCM. However, at the monthly scale the agreement between JMA AGCM output and observed data is unsatisfactory, especially for precipitation and especially in the dry season (e.g. MRB’s average JMA AGCM January rainfall (1979–1998) is 224Ð0% higher than the observed). At the daily scale, the JMA AGCM simulated precipitation data is entirely different to the observed—neither the frequency nor the magnitude of observed daily data is replicated by the JMA AGCM Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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Figure 5. Annual mean precipitation calculated using JMA AGCM (0Ð1875° ð 0Ð1875° grids) daily average precipitation for the periods (a) 1979– 1998 (i.e. ‘current’) and (b) 2080– 2099 (i.e. ‘future’). (c) The ratio of ‘future’ over ‘current’

Figure 6. Annual mean temperature calculated using the average of the daily maximum and minimum surface temperature from the JMA AGCM (0Ð1875° ð 0Ð1875° grids) for the periods (a) 1979– 1998 (i.e. ‘current’) and (b) 2080– 2099 (i.e. ‘future’). (c) The difference between ‘future’ and ‘current’

(Figure 7), or any other GCM (see for example Giorgi et al., 2001). Figure 7a demonstrates that the magnitude of extreme daily rainfall event (i.e. highest 10%) is significantly underestimated by the JMA AGCM for the MRB. In addition, Figure 7b shows that the number of 0–2 mm rainfall days is also underestimated by the JMA AGCM. These two points, along with the fact that the number of mid-range rainfall days (i.e. 40–60% probability exceedance) is overestimated to ensure annual JMA AGCM totals match the observed values, result in a precipitation time series that is ‘smoother’ than the natural sequence. Therefore, the direct use of daily JMA AGCM, or any other GCM, output as direct input for hydrological modelling is of questionable utility (see for example Giorgi et al., 2001). Copyright  2008 John Wiley & Sons, Ltd.

To further illustrate the problems associated with using ‘raw’ GCM data as input for hydrological models, the discharge at Pakse is simulated from 1979 to 1981 using YHyM (Takeuchi et al., 1999; Ao et al., 2003a,b; Hapuarachchi et al., 2004a,b, 2008; Zhou et al., 2006); first using the precipitation and temperature simulated by the JMA AGCM as input for YHyM (Figure 8a) and second using observed precipitation and temperature data as input (Figure 8b). Note in both cases the parameters and potential evaporation, and resulting simulated discharge, are exactly the same as in Hapuarachchi et al. (2008). In Figure 8a and b, the observed discharge at Pakse is also included for comparison. From Figure 8, it is clear that when ‘raw’ JMA AGCM precipitation is used as input for YHyM the discharge at Pakse is significantly underestimated. A Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp


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Figure 7. Comparison between the observed and the JMA AGCM precipitation (1979– 1981) over the area upstream from Pakse using (a) probability exceedance and (b) frequency histogram

Figure 8. Comparison between the observed discharge at Pakse (1979– 1981) and discharge simulated by YHyM using (a) ‘raw’ JMA AGCM daily average precipitation and temperature and (b) observed precipitation and temperature (Hapuarachchi et al., 2008)

possible explanation for this is the decreased magnitude of extreme (i.e. top 10%) rainfall events and also because when the JMA AGCM simulation is used, the saturation level of soil is higher than it is in reality (due to the underestimation of the number of 0–2 mm rainfall days) resulting in more surface pooling, and hence increased loss of water due to evaporation. Copyright  2008 John Wiley & Sons, Ltd.

In addition, though there is 20 years of ‘current’ and ‘future’ JMA AGCM output, this should not be interpreted as a 20-year long-time series. Rather, since both the ‘current’ and ‘future’ JMA AGCM scenarios are produced using an SST climatology, what the JMA AGCM output really represents is 20 different 1 year realizations of daily precipitation and temperature under Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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a ‘current’ climate and 20 different realizations under a ‘future’ climate. Reflecting climate change in hydrological model input data at the daily scale Since ‘raw’ GCM daily precipitation data is unsuitable as input for YHyM, a method is needed to enable GCM information to be used to simulate climate change in the hydroclimatology of the MRB. The simplest method for reflecting climate change in hydrological input data is to scale the observed variable on the basis of ‘changes’ estimated by (GCMs)—referred to as ‘constant scaling’ or the ‘delta change’ method (e.g. Gleick, 1986; Arnell, 1996; Chiew and McMahon, 2002; Prudhomme et al., 2002). This is the most commonly used method and involves a constant factor for each year, season or month being used to scale all the daily data in the relevant year, season or month. As mentioned, this method is very simple and therefore enables several different climate change scenarios and the output from several GCMs to be considered and run through the hydrological model. The main limitations of the ‘constant scaling’ method are that it ignores changes in the temporal rainfall distribution and changes in the number of wet days. In addition, using the ‘constant scaling’ method gives a false indication of the changes in daily extremes, which are of particular interest in hydrology and under a warming climate are likely to have a higher increase in magnitude than non-extreme events (Stocker et al., 2001). To overcome these limitations, the stochastic weather generator and stochastic downscaling approaches can be used. However, both these approaches are complex and extremely time-consuming, especially when utilizing output from several GCMs and/or several different scenarios. For more detail on the advantages and limitations of the stochastic weather generator and stochastic downscaling approaches, see Chiew et al. (2003), Harrold and Jones (2003) and Harrold et al. (2005a). To estimate ‘climate change impacted precipitation’ over the MRB, and to avoid the problems associated with the method mentioned above, this study utilizes the ‘daily scaling’ method (Chiew et al., 2003; Harrold and Jones, 2003; Harrold et al., 2005a). The ‘daily scaling’ method is a refined version of the ‘constant scaling’ method, in which the pattern of change in the ranked JMA AGCM daily MRB precipitation is used to scale the ranked historical MRB daily rainfall. The ‘daily scaling’ method is sensitive to changes in extreme daily rainfalls and changes in the frequency of wet days simulated by the GCM, and therefore produces a more realistic sequence of changed (i.e. future) daily rainfall, compared to the ‘constant scaling’ approach where all historical values in each year, season or month are scaled by the same amount. Like the ‘constant scaling’ method, the ‘daily scaling’ method does not consider changes in the temporal distribution of precipitation. This is a flaw that requires further investigation, particularly when applying the method to Copyright  2008 John Wiley & Sons, Ltd.

regions where multi-temporal variability is marked (e.g. the MRB). The daily scaling method is applied separately at each precipitation station and month. Figure 9 illustrates the ‘daily scaling’ method for July at station 150306 (103Ð2 ° E; 15Ð5 ° N), which is located in Thailand (subbasin 6). Ranked JMA AGCM precipitation at the grid containing station 150306 and from all days in July for 1979–1998 (Figure 9a) and for 2080–2099 (Figure 9b) is compared. The ranked differences are then expressed as ratios relative to 1979–1998 values (Figure 9c). The ranked daily pattern of change in JMA AGCM rainfall (Figure 9c) is then used to scale the 29 years (1972–2000) of ranked historical daily precipitation (Figure 9d) to provide 29 years of daily precipitation in a 2080–2099 climate (Figure 9e)—in this example for July at station 150306. If, for the station and month being ‘daily scaled’ the JMA AGCM indicates a decrease (of say x%) in the number of wet days, which are defined as >0Ð5 mm to avoid problems associated with GCM ‘drizzle’ (Harrold et al., 2005b), then the lowest x% of wet days in the ranked historical daily precipitation (Figure 9d) is set to dry days prior to scaling. If the JMA AGCM indicates an increase (of say x%) in the number of wet days then x% of dry days is chosen at random for the given month and station and given a value of 0Ð1 mm prior to ‘daily scaling’. Therefore, the 29 years of ‘future’ daily precipitation (Figure 9e) have the same temporal structure as the ‘current’ precipitation, but the precipitation amount on each day is scaled using a factor chosen from the appropriate position in Figure 9c and the change in the frequency of wet (>0Ð5 mm) days is consistent with that indicated by the JMA AGCM. The final step in the ‘daily scaling’ method is to use 12 ratios (one for each month) to adjust the ‘future’ MRB rainfall to ensure that the average monthly changes at each station are the same as the average monthly changes estimated by the JMA AGCM. Figure 10 shows simulations obtained from YHyM for 5 years’ discharge at Pakse under ‘future’ climate conditions using (i) constant ANNUAL scaling, (ii) constant MONTHLY scaling and (c) daily scaling, where scaled precipitation at each station is spatially distributed in YHyM using the Thiessen polygon method and future change of air temperature is not taken into account for this simulation in order to see the pure impact of precipitation change. There is very little difference between the hydrographs in Figure 10a and b, suggesting that any future hydroclimatological change at Pakse will be consistent across all months. Importantly, Figure 10c (using the ‘daily scaled’ precipitation as input for YHyM) shows a hydrograph that is markedly different to the hydrographs obtained using ‘constant scaling’. In particular, the peaks are totally different when the ‘daily scaling’ method is used. It is assumed that using ‘daily scaled’ data provides the most realistic estimate of future hydrological conditions. This is because the ‘daily scaling’ method specifically accounts for changes in the frequency of wet days and changes in extreme precipitation (rather Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp


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Figure 9. Illustration of the ‘daily scaling’ method used in this study for July at station 150306 (103Ð2 ° E; 15Ð5 ° N)

than assuming the number of wet days does not change and that changes in precipitation magnitude will be uniform across all probability exceedance levels). When using the constant scaling method, all daily rainfall is factored by the same constant. Thus, extreme rainfall days are increased (or decreased) in absolute terms by a greater amount (in mm) than the smaller rainfalls. On the other hand, with daily scaling each daily rainfall amount is increased based on where it is ‘ranked’ and the very wet days are not factored up by unrealistic amounts. Therefore, it is obvious from Figure 10 that any water resources management or flood mitigation strategies developed based on the simulations obtained using ‘constant scaling’ (Figure 10a and b) would be severely erroneous. Copyright  2008 John Wiley & Sons, Ltd.

Future hydrological conditions in the Mekong River basin Figure 11 shows for the MRB (and each subbasin) the change in annual average number of wet days (WD), annual average precipitation (P) and annual average discharge (Q) between ‘now’ (averages based on 29 years (1972–2000) of discharge simulated using observed precipitation and temperature—see Hapuarachchi et al., 2008) and the ‘future’ (averages based on 29 years of discharge simulated using observed precipitation that has been ‘daily scaled’ to reflect the change indicated by the 1979–1998 and 2080–2099 JMA AGCM simulations). As a whole it appears that, under the ‘future’ JMA AGCM scenario, annual average wet days, precipitation and discharge in the MRB will increase by 5Ð2, 6Ð3 and Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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Figure 10. Simulation of 5 years’ discharge at Pakse under ‘future’ climate conditions using (a) constant ANNUAL scaling, (b) constant MONTHLY scaling and (c) daily scaling

11Ð7%, respectively. All subbasins show an increase in the annual average number of wet days and all, apart from subbasin two and three, show an increase in annual average precipitation and discharge. Interestingly, for the MRB as a whole, and each subbasin except subbasins zero, one and two, the changes in discharge indicated in Figure 11 compare well with the expected change in discharge calculated by multiplying the change in precipitation by the ‘elasticity’ (Table I; Hapuarachchi et al., 2008). This implies that the widely used ‘elasticity’, which is a function of precipitation and streamflow (Sankarasubramanian et al., 2001), is not applicable to the northern MRB because temperature, via its influence on snow accumulation and melt, plays an important role Copyright  2008 John Wiley & Sons, Ltd.

in the precipitation to runoff transformation in subbasins zero, one and two. This result also confirms the findings of Kiem et al. (2005) who showed that the impact of snow accumulation and melting (which is controlled by temperature) on runoff totals in the MRB is significant, even though snow covers on average (1981–2000) only 5Ð1% of the MRB from November to March (and is almost non-existent outside this period). Table II shows for the MRB (and each subbasin) the change in monthly average number of WDs, monthly average precipitation (P) and monthly average discharge (Q) between ‘now’ (averages based on 29 years (1972–2000) of discharge simulated using observed precipitation and temperature—see Hapuarachchi et al., Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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Figure 11. Change in annual average number of wet days (WDs), annual average precipitation (P) and annual average discharge (Q) between ‘now’ (averages based on 29 years (1972– 2000) of discharge simulated using observed precipitation and temperature) and the ‘future’ (averages based on 29 years of discharge simulated using observed precipitation that has been ‘daily scaled’ to reflect the change indicated by the 1979– 1998 and 2080– 2099 JMA AGCM simulations). Basin average WDs over the basin were calculated on the spatially interpolated (using Thiessen polygon) precipitation data. YHyM outputs the amount of rainfall on each day across the whole basin (and each subbasin) and this was used to calculate basin average WD and change in WD

2008) and the ‘future’ (averages based on 29 years of discharge simulated using observed precipitation that has been ‘daily scaled’ to reflect the change indicated by the 1979–1998 and 2080–2099 JMA AGCM simulations). Table III illustrates the impact of climate change, as simulated by the JMA AGCM and the ‘daily scaling’ method, on some important hydrological statistics in the MRB and each of the subbasins. Table III shows that over the whole MRB (and in six out of seven subbasins) the magnitude of the maximum flood will decrease. However,

since this statistic is based on only one value, a better indication of the change in magnitude and frequency of ‘high’ flows is given by the change in Q10 magnitude and the change in the number of days in the ‘future’ that is above the ‘current’ Q10. Q10 refers to the value that 10% of daily streamflows are higher than (i.e. days above this level would be considered floods). Similarly, Q90 refers to the value that 90% of daily streamflows are higher than (or 10% of values are less than). From Table III, it can be seen that over the MRB as a whole (and for all subbasins except two and three) there is an increase in the Q10 and Q90 values under the future JMA AGCM scenario. In other words, there will be a greater number of days where the discharge is above the level that is now considered extremely high (i.e. ‘current’ Q10) and therefore increased likelihood of flooding, even though maximum flood magnitude is reduced, across the MRB in all but subbasins two and three. On the other hand, in all but subbasin 3, there will also be a reduction in the number of days where the discharge is below what is now considered as an extremely low level (i.e. ‘current’ Q90) and therefore a reduction in the severity of droughts/low-flow periods is expected, assuming water extraction (agriculture, power generation reservoirs, domestic and industrial use, etc.) is kept at a sustainable level. Therefore, from these results, it appears that a comprehensive reanalysis of expected 21st century MRB flood and drought risk (i.e. frequency and magnitude) is needed—since it is highly unlikely that current estimates (e.g. 1 in 100 year flood) will be valid in the future.

DISCUSSION AND CONCLUSIONS Analysis of future JMA AGCM climate scenarios for the MRB indicates that the annual mean precipitation will increase in the 21st century (2080–2099) by on average 4Ð2% across the basin, with the majority of this increase occurring over the northern MRB (i.e. China). Annual mean temperatures are also projected to increase by approximately 2Ð6 ° C (averaged across the MRB). As

Table I. Expected change in annual discharge between ‘now’ (1979–1998) and the ‘future’ (2080–2099) based on the change in annual precipitation and the calculated ‘elasticity’. See Hapuarachchi et al. (2008) for details on the derivation of MRB ‘elasticity’ Basin

0 (7 stations) 1 (9 stations) 2 (10 stations) 3 (14 stations) 4 (8 stations) 5 (3 stations) 6 (13 stations) 7 (0 stations) 8 (1 stations) MEKONG (65 stations)

Copyright  2008 John Wiley & Sons, Ltd.

Change in annual precipitation (%)

Elasticity

Expected change in annual discharge (%)

C24Ð1 C5Ð3 C1Ð4 3Ð7 C4Ð4 C10Ð4 C5Ð2 C10Ð0 C14Ð1 C6Ð3

1Ð26 1Ð86 1Ð60 2Ð42 1Ð64 1Ð92 2Ð07 2Ð10 1Ð70 1Ð99

C30Ð4 C4Ð3 C2Ð2 9Ð0 C7Ð2 C20Ð0 C10Ð8 C21Ð0 C24Ð0 C12Ð5

Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp

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Table II. Change in monthly average number of wet days (WD), monthly average precipitation (P) and monthly average discharge (Q) between ‘now’ (1979–1998) and the ‘future’ (2080–2099) BASIN Month 1 2 3 4 5 6 7 8 9 10 11 12

WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q WD P Q

MRB

0

1

2

3

4

5

6

7

8

26Ð9 24Ð8 12Ð2 5Ð1 1Ð0 10Ð7 0Ð3 20Ð1 8Ð4 0Ð3 3Ð3 9Ð9 0Ð0 0Ð1 4Ð3 0Ð0 6Ð1 24Ð5 0Ð0 13Ð1 10Ð4 0Ð0 6Ð6 15Ð8 0Ð0 0Ð6 6Ð7 1Ð2 9Ð7 15Ð5 3Ð1 18Ð2 9Ð8 39Ð7 29Ð8 5Ð7










Table III. Change in maximum flood magnitude, Q90, Q10 and the number of days below (above) ‘current’ Q90 (Q10) levels under a ‘future’ climate Basin

0 1 2 3 4 5 6 7 8 Whole MRB

Change in maximum flood magnitude (%)

Change in Q90 flow magnitude (%)

Number of days below ‘current’ Q90 in the ‘future’

Change in Q10 flow magnitude (%)

Number of days above ‘current’ Q10 in the ‘future’

39Ð1 13Ð2 6Ð7 15Ð4 9Ð3 0Ð6 41Ð2 52Ð9 24Ð2 11Ð8

20Ð3 14Ð2 8Ð2 0Ð4 1Ð5 5Ð2 1Ð9 8Ð2 11Ð6 9Ð8

12 105 787 1094 921 745 938 646 634 424

76Ð3 18Ð5 3Ð3 14Ð0 9Ð1 20Ð0 15Ð2 18Ð9 38Ð5 9Ð6

2731 1208 1026 853 1179 1337 1193 1396 1516 1468

Note: ‘current’ number of days below (above) ‘current’ Q90 (Q10) levels is 1059 (i.e. 10% of 29 years ð 365 days).

expected, these changes also lead to significant changes in the hydrology of the MRB with increases in the annual discharge projected in all basins except subbasin three. Copyright  2008 John Wiley & Sons, Ltd.

The changes to annual average discharge were estimated well by the calculated elasticity (Hapuarachchi et al., 2008), except in the subbasins where temperature plays a Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp


major role in the precipitation to discharge transformation via snow accumulation and melting (e.g. subbasins zero, one and two). In addition to annual totals changing under a future climate, the precipitation ‘pattern’ is also expected to change, with all subbasins experiencing an increase in the number of wet days in the ‘future’ JMA AGCM scenario. Importantly, for sustainable water resources management and the mitigation of extreme events (e.g. floods and droughts), the magnitude and frequency of which are now considered extreme events are also expected to change markedly. Under a 21st century (2080–2099) climate, increased flooding (i.e. increased number of events greater than current Q10) is expected across the MRB in all but subbasins two and three while a reduction in the severity of droughts/low-flow periods is expected (except in subbasin 3), assuming water extraction (agriculture, power generation reservoirs, domestic and industrial use, etc.) is kept at a sustainable level. It is important to note that the method used to reflect climate change in the daily data (i.e. the ‘daily scaling’ method) that was then used to produce the 21st century MRB hydrological simulations does not account for temporal variability in the distribution of hydrological input data (e.g. precipitation and temperature). Therefore, changes to the magnitude and frequency of natural sources of climate variability (e.g. the El Ni˜no/Southern Oscillation—ENSO) are not considered. Currently, methods to address this problem via the use of coupled ocean-atmospheric GCMs (i.e. SST that varies) and a refined version of the ‘daily scaling’ method are being investigated. In addition, the JMA AGCM scenarios assume vegetation is the same in the ‘future’ as it is now. Likewise, vegetation, potential evaporation and parameter values are the same in both ‘current’ and ‘future’ YHyM runs. The significance of these uncertainties and assumptions is currently being investigated. It should also be mentioned that while daily data is sufficient for the objectives of this study it is often desirable (sometimes essential depending on region, catchment characteristics, hydrological model, application etc) in hydrological modelling to use sub-daily data. Therefore, the investigation and development of methods that realistically incorporate the impacts of climate change at the sub-daily scale is an area that requires urgent attention. In any case, since the results shown here are based on the output of one climate model operating under one future scenario, they should not be interpreted as a prediction of what ‘will’ happen in the MRB in the 21st century. Instead, they should be seen as one possibility of what ‘could’ happen (if the SRES A1B scenario actually occurs, if the 1981–1993 SST climatology is an adequate estimate of ‘current’ SST conditions, if vegetation type and quantity remains as is, etc.). Gaining a better understanding of what ‘could’ happen (the results presented here are just one possibility), rather than pretending we know what ‘will’ happen, will be extremely useful for ensuring sustainable development and improved water Copyright  2008 John Wiley & Sons, Ltd.

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resource management in the MRB. In order to do this, future change in hydroclimatological variables needs to be projected based on output from several different climate models operating under a variety of scenarios. This will give a more complete picture of 21st century hydroclimatological conditions and also some insight into the uncertainties associated with projecting the impacts of climate change. Nevertheless, based on the results in this study, it appears that policies, infrastructure and mitigation strategies need to be put in place now to protect against the increased MRB flooding that is likely to occur in the future. In addition, despite this study indicating a decrease in the number of ‘low-flow’ days, across most of the MRB, further analysis is needed to determine whether the reduction in low-flow days is enough to compensate for (and sustain) the rapidly increasing population and development in the MRB.

ACKNOWLEDGEMENTS

The JMA AGCM climate experiments were conducted under the framework of the Research Revolution 2002 (RR2002) ‘Development of Super-High-Resolution Global and Regional Climate Models’ project. This project was funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT). The authors also gratefully acknowledge the Japan Society for the Promotion of Science (Anthony Kiem) and the 21st Century Centre of Excellence (COE) Program at the University of Yamanashi (Hapuarachchige Hapuarachchi) for their financial support.

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Hydrol. Process. 22, 1382– 1394 (2008) DOI: 10.1002/hyp