1.5.1 General objectives
The water resources in the eastern seaboard of Thailand are crucial for both industry and agriculture. The water supply then becomes a primary element for promoting and developing economical production. Eventually, the availability of a secure water management system is a major factor for industrial-investment considerations. In this study, the investigation of climate variations will not only reveal possible future water crises due to long periods of water shortage, but also put forward hydrological risks by either droughts or floods, and will, at the very end, help to develop sustainable water-resource planning strategy in the wake of climate change that already has taken place and will do more so in the future.
To evaluate such climate change effects, various downscaling and forecasting tools are developed to predict the local climate in the study area. In addition, the possible tele-connection between ocean state indices and local climate are also investigated to introduce a new tool for seasonal climate prediction in the region. The assessments of climate change impacts on water resources;
including shortages and excesses, requires both long-range information for project management, and short-range data for operational decisions. Therefore, a comprehensive development of short- and long-term local climate predictions is carried out in this study.
In addition, in account of the interaction between surface water and groundwater, changes of the atmospheric climate will not only affect surface water but also groundwater. Because these two hydrological processes are complexly linked, a comprehensive analysis of climate-driven surface- as well as of subsurface water potential will be done. The assessment of climate change impact on the overall water resources, both surface water and groundwater recharge, are examined by coupling downscaled climate- models with appropriate hydrological models.
With this, the goals of this study can be summarized as following:
1. Develop climate prediction tools for a local area to forecast local weather for short- and long range
2. Predict the local climate in the study area over the 21st-century
3. Investigate the impact of climate change on water resources over the 21st-century
1.5.2 Scope of the research work
As mentioned in the previous section, the effects of future climate change at the watershed-scale in the eastern seaboard of Thailand are studied, whereby the focus is on the evaluation of the long-term sustainability of the water resources in the area. To achieve this goal the following six topical steps need to be executed:
a) Study of the local climate variations and their relationships with ocean state indices of the Pacific and/or the Indian ocean
b) Development of seasonal prediction tools to forecast the local weather pattern by using statistical and correlative relationships of climate parameters for short- and long-range forecasting
c) Set-up of surface and soil water simulation models that generate streamflow and other hydrological components in the KY basin using observed or predicted atmospheric input parameters
d) Prediction of local climate and streamflow in the study area by long-term climate forecasting up to the end of 21st-century using downscaled predictors from various GCMs based on IPCC (SRES) emission scenarios A1B, A2 and B1
e) Investigation of the future water budget and the potential water supply in the wake of possible climate changes as predicted by the GCM models under the three SRES -scenarios
The theoretical background and the specific processes to achieve these goals study are described in the Chapters 2 to 6.
Methodology and thesis structure 1.6
The local climate is predicted until the end of 21st-century and then applied as input for the hydrological simulations. The impact of climate change on water resources is assessed via the SWAT model. The assessment processes can be divided into five topical steps, as shown in Figure 1.2, which expresses the thesis structure and the methods used. In the following, further explanations on the methodologies to carry out these topics and the thesis structure are presented:
1. Climate diagnosis (Chapter 2 and 3)
a. Local climate reconstruction (Section 2.3)
The existing date of climate data is complied with multi-linear regression models to fill in the missing values in the climate time-series
b. Local climate analysis (Chapter 2)
Spatial and temporal properties of the local climate are analyzed by employing time-series analysis and wavelet transforms to explore the changes in climate variation
c. Teleconnections (Chapter 2)
Cross-correlation analysis between local climate and ocean state indices is performed to investigate possible relationships between the two
2. Prediction tool development
a. Long-term climate predictions (Chapter 3)
Conventional downscaling models such as SDSM and LARS-WG, as well as newly developed models, such as multi-linear regression (MLR) models that can use multi-GCM- predictor ensembles are applied to generate future climate scenarios in the study region
b. Short-term climate predictions (Chapter 4)
Autoregressive models, i.e., AR, ARIMA and ARIMAex and multiple linear regression (MLR) model are used for near-future forecasts of local climate time-series. Incorporating with possible teleconnection from ocean state indices and atmospheric climate predictors from GCMs, the predictor-choices used in short-term climate prediction consist of:
Ocean state indices from Pacific and Indian Oceans
Coarse- and high-resolution GCMs
The model which provides the best prediction skill is the applied in short-term climate forecasting in the study area.
3. Daily climate generation (Chapter 5)
a. Development of a new multi-site stochastic daily weather generator (DWG) This new DWG takes into account the geospatial correlation properties of the climate variables for multi sites across a region, to produce daily climate time series from monthly observed or predicted climate variables
b. Multi-site daily precipitation and temperature generation (Sections 5.2 and 5.3) Daily precipitation and temperature-time-series are generated from their monthly counterparts, using the statistical attributes of the observed time series in the new weather generator monthly. After calibration and validation the weather generator is then applied to generate future daily climate series from downscaled monthly predictions of the various GCMs used in this study.
4. Climate projection (Chapters 3 and 5)
The tools which are developed for long-term climate prediction and the generation of daily climate series are employed to generate future daily climate series for the study region, using two processes: downscaling and daily resampling.
Firstly, monthly climate time-series are obtained through downscaling of the GCM-produced long-term predictors, obtained in Chapter 3.
Subsequently, in Chapter 5, the predicted monthly data is resampled and 30 realizations of daily climate series under the SRES- scenarios A1B, A2 and B1 are generated and analyzed.
5. Impact assessment (Chapters 6 and 7) a. Hydrologic study (Chapter 6)
SWAT modeling is performed to simulate various hydrologic components, namely, streamflow, water yield and groundwater recharge in the study basin
b. Possible change of future climate (Chapter 7)
The variation and change of climate in 21st-century concerning to the trend, statistical distribution and extreme event are summarized
c. Future water resources (Chapter 7)
Hydrological changes over the 21st-century due to impacts of climate change are examined by incorporating the future climate predictors as input in the SWAT model
Figure 1.2. Overview of the research methodology used to predict future climate and to investigate the ensuing impacts on water resources in the study region.