1.3.1 Climate and hydrological predictions
With regard to the proper hydrological forecasting for water resources management, the prediction of surface water inflow into a reservoir (Yao and Georgakakos 2001) and the recharge of groundwater (Changnon et al. 1988, Scibek and Allen 2006) are then of major concern when studying the impact of climate change on water resources. The precipitation is the primary forcing variable in such hydrological models. This causes a particular problem since this meteorological parameter is fraught with a lot of uncertainty as an output parameter of global or regional climate models. To maintain the reliability of future water resources predictions, the input time-series in a hydrological model should then reflect as best a possible the potential precipitation changes due to global or local climate change (Yao and Georgakakos 2001).
To evaluate sustainable and efficient managing strategies for water resources, the hydrological uncertainties in a hydrological forecasting model are estimated by various authors using different approaches, such as likelihood-measured inputs (Beven and Binley 1992), Monte Carlo approaches for forecasting reservoir inflow (Georgakakos et al. 1998, Kuczera and Parent 1998) or a Bayesian framework to quantify input parameters (Ajami et al. 2008). In case of long-term forecasting, a complex climate model that is able to simulate the spatial and temporal distribution of climate variables dynamically is necessary to predict and plan for future water resources management strategies (Houghton et al. 2001, Serrat-Capdevila et al. 2007).
For far-future climate assessments on a century-long time-scale, climate predictions are usually done with the help of general circulation models (Phillips 1956) or global climate model (GCMs) which generate future climate scenarios by simulating daily climate time-series (temperature, precipitation, etc.) at a global-scale. (Wilby and Wigley 1997). However, due to the coarse resolution of the global GCM’s and, still, insufficient parameter representation, the output from GCMs is the least reliable for subsequent hydrological applications. Hydrological models are usually applied on the catchment-scale which then requires also regionally-scaled parameters (Wilby et al. 1999). One approach to do this properly is the application of so-called techniques of downscaling (Wigley et al. 1990, Wilby and Wigley 1997). Spatial and temporal downscaling techniques are usually applied (Giorgi and Mearns 1991, Robock et al. 1993, Hewitson and Crane 1996, Wilby and Wigley 1997) to obtain finer resolutions of climate data as provided by the global GCMs. After downscaling, the driving input for hydrological modeling is available to a certain degree.
In Thailand, Singhrattna (Singhrattna and Singh Babel 2011) predicted monsoon rainfall in the north of Thailand using GCMs. Aksornsingcha and Chutime (Aksornsingcha and Chutime 2011) also predicted the climate for most of Thailand,, excluding the eastern seaboard region which, in fact, is the study area in this thesis. These authors used a multi-linear regression downscaling GFDL CM2.x model for 25 climate stations covering most (but the study region) of Thailand.
During the last decade, research on the effects of climate change on water resources has expanded significantly. Changes in temperature and precipitation due to climate change have been predicted and their impacts on hydrologic system have been studied. The majority of these studies has focused on surface hydrological components (Arnell 1999, Zhang et al. 2011a); such as runoff(Semenov and Bengtsson 2002, Labat et al. 2004), streamflow (Jha et al. 2004, Wang et al. 2011) and water yield (Stone 2003, 2005), whereas only a few are devoted to the potential impacts on groundwater aquifers (Koch 2008a). For example, the impact of climate change on groundwater, due to changes in the rate of recharge, was analyzed by Scibek and Allen (Scibek and Allen 2006). Other studies include that of Brouyére et al. (Brouyére et al. 2004) for the recharge across Belgium, of Scibek and Allen (Scibek and Allen 2006) for Canada, of Jyrkama and Sykes (Jyrkama and Sykes 2007) for the US and of Herrera and Hiscock (Herrera-Pantoja and Hiscock 2008) for the UK.
Other studies employed relationships between regional climate, as defined by the monsoon occurrence and ocean state indices, such as the SST or the El Niño–Southern Oscillation (ENSO) (Rasmusson and Carpenter 1983, Kumar 1999) establishing the connection of local climate and ocean climate (Quinn et al. 1978, Su et al. 2001), i.e. so-called teleconnections. Such a connection expresses the relationship between the ocean climate with the monsoon (Ju and Slingo 1995) or the precipitation (Quinn et al. 1978, Ropelewski and Halpert 1987) over wide areas, namely, Asia (Ju and Slingo 1995), America (Mo 2003), Europe (Trigo et al. 2004), Put in Markovic and Koch (2005), Australia (Samuel et al. 2006) and Africa (Odekunle and Eludoyin 2008). In the beginning, the relationships between the Pacific Ocean climate and the south Asia monsoon were established (Rasmusson and Carpenter 1983, Shukla and Paolino 1983), as well as the subsequent precipitation (Quinn et al. 1978, Ropelewski and Halpert 1987, Mo 2003, Nguyen et al. 2007). Later, the climate of the Indian Ocean was also related to the Asian monsoon (Yoo et al. 2006) and to the South Asian precipitation (Clark et al. 2000, Samuel et al. 2006). In addition, teleconnections between Atlantic Ocean climate and rainfall in Germany (Markovic and Koch 2005) and South America (Barreiro and Tippmann 2008) were set up. In Thailand, Singhrattna (Singhrattna et al. 2005b) found relationships of the summer monsoon rainfall with the Pacific sea surface temperatures and ENSO signals during recent decades. These studies suggested to use dynamic relationships to predict seasonal weather pattern across the country
based on the Pacific Ocean station (Shukla and Paolino 1983, Kumar et al. 1995, Clark et al.
2000).
In more recent studies these tele-relationships were employed to perform near-future climate- and hydrologic predictions (Goddard et al. 2001, Rimbu et al. 2005, Samuel et al. 2006, Xu et al.
2007, Singhrattna and Singh Babel 2011, Poveda et al. 2011). While the regional climate is controlled by variation of global-scale circulation, the climate teleconnection contributes relationship driving predictable part of precipitation and temperature (Goddard et al. 2001). For Thailand, Singhrattna (Singhrattna and Singh Babel 2011) forecasted rainfall using a k-nearest neighbor model and Pacific Ocean SST climate predictors. In modern study, teleconnections to enhance short-term predictions can not only be used for climate assessment, but also for hydrological forecasts (Dettinger and Diaz 2000). Thus, streamflow forecasts in Middle East (Cullen et al. 2002), Europe (Trigo et al. 2004, Rimbu et al. 2005) and China (Xu et al. 2007) using interconnections with SST and ENSO have been done by several authors. Nevertheless, the use of such large-scale teleconnection has not yet been applied in hydrological prediction over Thailand that is withal necessary for planning water policy in the wake of climate change.
As suggested by Fowler et al. (Fowler et al. 2007), the climate information used in climate change model and climate downscaling is ably applied with hydrological modeling as a decision making tool for planning and managing water resources. The integrated use of potential teleconnection, climate downscaling and hydrologic model can therefore provide a comprehensive prediction tool for short- and long-term climate and hydrological prediction in Thailand.
1.3.2 Water resources management in the wake of climate change
It is becoming clear nowadays, that water management in many regions of the world must become more efficient to be able to guarantee the future local food supply, not to the least due to the detrimental impacts of climate change. For example, water management in Thailand as well as in many other regions in the world is practically operated under a rule curve which is statistically calculated based on historical climate data. It is clear that such an approach will not be able to take into account the effects of recent climate change which, therefore, will lead to a non-optimal use of the water resources in the long run (Georgakakos et al. 1998, Rosegrant et al.
2002).
In most applications of water resources management future long and representative hydrological time series are created from a synthetic time-series generated from the historical records (Jettmar and Young 1975). As a matter of fact, since in the long-term, there is a climatic trend of a decreasing rainfall in Thailand (McCarthy 2001, Vongvisessomjai 2010), the historical supporting data might be misleading for use in future planning. A solution to this problem must be then the reliable prediction of the local future climate and its effect on the water resources (Georgakakos et al. 1998) in the study region.
The research proposed in the present thesis addresses the possible impacts of climate change on surface water and groundwater recharge in the study region by complex hydrological models which integrates the surface water and soil water, similar to the model approach used by Bejranonda et al. (Bejranonda et al. 2007b) for the upper great Chao Phraya plain basin in central Thailand. However, groundwater-surface water interaction plays often an important role, particularly, for shallow aquifers with interceding streams. In such cases, a full two-way coupling of the surface water and subsurface water models is required, as done by Bejranonda et al., (Bejranonda et al. 2007b) who coupled the SWAT-surface water model with the MODFLOW groundwater model. In fact, the studies of Bejranonda et al (Bejranonda et al. 2007b) and Changnon and Hsu (Changnon et al. 1988) show that the water table in a shallow aquifer which is often the main groundwater source for agricultural irrigation is most sensitive to recharge and
pumping. Thus variations of groundwater recharge (Scibek and Allen 2006, Alexander and Palmer R.N. 2007, Jyrkama and Sykes 2007) and changes of groundwater extraction will induce groundwater level fluctuations that may be indicative of climate change impacts on groundwater, (Brouyére et al. 2004)i.e. there might be significant long-term implications on the groundwater potential. In any case, for the proper simulation of the latter in a varying climate, dynamic linking of climate- with surface and soil water models should be endeavored. This is one of the major objectives of the present thesis.
Some studies addressed the integration of surface water and groundwater as they looked at climate change impacts on both parts (Scibek and Allen 2006, Koch 2008b). Most of the authors of the studies mentioned deplore the lack of investigations on the impacts of climate change on groundwater resources and suggest that more comprehensive evaluation on water resources should be undertaken (Alexander and Palmer R.N. 2007, Koch 2008b).
As discussed earlier, the only major storage of water supply in the eastern seaboard of Thailand is by the network of reservoirs. Koontanakulvong et al. (Koontanakulvong 2010) have pointed out the possibility of insufficient surface water in the study region in the near future; meanwhile, a kind of stop-gap or emergency resource is groundwater extraction. On the other hand, in many other regions of the world, including the central plain area of Thailand, groundwater plays an important role for emergency water supply (Bejranonda et al. 2007a). In that part of Thailand, the increased irrigation for augmenting rice production has led to the development of so-called conjunctive use schemes of surface and groundwater (Koontanakulvong 2006, Bejranonda et al.
2007a).
Conjunctive water management requires the use of full-range hydrological continuous-time processes at the catchment scale (Bejranonda et al. 2013). Physically based hydrologic models that can be used for that purpose and which are able to estimate potential groundwater recharge at the regional scale, are, for example, HELP3 (Schroeder et al. 1994), SWAT (Arnold et al. 1998, Neitsch et al. 2005) and MIKE SHE (Hughes and Liu 2008). Among these, SWAT is freeware and this may be one of the reasons why it has been widely in numerous hydrological applications on a catchment scale to compute streamflow and recharge. The SWAT model is also selected here to simulate the hydrological conditions in the study basin in the eastern seaboard of Thailand.