2.2.1. CMIP5 Model

GCMs (General Circulation Models) are useful for describing and forecasting future climate change patterns. The World Meteorological Organization's Global Climate Research Program is now collecting data on current global climate change under the acronym Coupled Model Intercomparison Project Phase 5 (CMIP5) [31]. For this study, 10 CMIP5 models were selected by the investigators: MIROC\_ESM, BNU, CanESM, MIROC5, FGOALS\_g2, CESM1\_CAM5, GFDL, EC\_EARTH, CCSM4, and FGOALS\_s2 [32,33]. The data used in the global climate change analysis were supported by the Hydro-Informatics Institute (HII) (Public Organization), which revealed that there are a wide variety of models that can be applied (more than 15 models). However, when comparing the model's data with the measurement stations in the study area, (especially rainfall data) and ranked based on the lowest tolerance. It was found that the models used in this study were among the 10 models with the lowest inaccuracies and were used in this study. Then, in the streamflow analysis, only the climate data from five of the best models were selected. For ease of use, the HII, which has downscaled the data model to a 5 × 5 square kilometer grid. Base year climate data in the study areas used the data for 9 years between 2011–2019, and climate forecasting data from 30-year models between 2020–2049.

#### 2.2.2. Data Bias Correction

The Gamma-Gamma transformation approach was used in this study to correct for rainfall inaccuracy from the GCM. For this study, climate data, particularly precipitation data, courtesy of the Hydro-Informatics Institute (HII), is the agency that produces and distributes data for use in climate change studies in Thailand. This agency has identified the Gamma-Gamma transformation method to mitigate discrepancies in rainfall data. In addition, HII has published a study that applied this method to study the impact of climate change in Thailand on agricultural water demand [34]. In addition, Sharma (2015) has also chosen this method to study rainfall in western Thailand, which found that the Gamma-Gamma transformation was more effective in improving rainfall frequency and intensity compared to other methods [35]. The concept of this method is to correct for discrepancies

caused by frequency and rainfall between GCM and measurement stations in the base year by creating a cumulative distribution function (CDF). This leads to the creation of appropriate Gamma parameters, with the functionalities and key parameters as shown in Equations (1)–(4).

$$F(\mathbf{x}; \boldsymbol{\alpha}, \boldsymbol{\beta}) = \frac{1}{\beta^{\alpha} \Gamma(\alpha)} \mathbf{x}^{\alpha - 1} \exp\left(\frac{\mathbf{x}}{\beta}\right); \mathbf{x} \ge \mathbf{x}\_{\text{Trunc}} \tag{1}$$

$$F(\mathbf{x}; \mathbf{a}, \boldsymbol{\beta}) = \int\_{\mathbf{x}\_{\rm Tunc}}^{\mathbf{x}} f(\mathbf{t}) d\mathbf{t} \tag{2}$$

$$F(\mathfrak{x}\_{\mathsf{GCM}}; \mathfrak{a}\_{\prime} \beta | \mathsf{GCM}) \Rightarrow F(\mathfrak{x}\_{\mathsf{His}}; \mathfrak{a}\_{\prime} \beta | \mathsf{His}) \tag{3}$$

$$\mathbf{x}'\_{\rm GCM} = \boldsymbol{F}^{-1}\left\{\boldsymbol{F}(\mathbf{x}\_{\rm His}; \boldsymbol{\alpha}, \boldsymbol{\beta}|\boldsymbol{His})\right\} \tag{4}$$

where *α* is the shape and *β* is the size of the data from the GCM and base year monitoring stations at the selected locations to be gamma distribution. xTrunc is the amount of rainfall from CDF treated with the Gamma parameters, which are developed in Equation (2) for Equation (3). The *α* and *β* values were calculated by applying the maximum likelihood estimation method to calculate the daily precipitation from the inverse-adjusted GCM as shown in Equation (4).

#### *2.3. SWAT Hydrological Model*

The SWAT (Soil and Water Assessment Tool) model was created to aid in the management of water resources, and it was utilized in the evaluations for estimating the impact of water resource management and water pollution in watersheds and large basins [36], the quantity of streamflow that has changed, the amount of sediment and water quality in streams affected by changes in land use and climate in both past, present and future projections [37], which could be divided into distinct stages of watershed processing. For example, in the main watershed, sub-watershed zones are being created. Calculations that demonstrate outcomes daily and at extended intervals are also included. This considers variables from hydrological processes with the water balance equation as in Equation (5).

$$\text{SW}\_{\text{t}} = \text{SW}\_{0} + \sum\_{i=1}^{\text{t}} \left( \text{R}\_{\text{day}} - \text{Q}\_{\text{surf}} - \text{E}\_{\text{a}} - \text{W}\_{\text{seep}} - \text{Q}\_{\text{gw}} \right) \tag{5}$$

where SWt is the final soil water content; SW0 is the initial soil water content, t is the time (days), Rday is the precipitation (mm) on the day i, Qsurf is the surface water content on the day I, Ea is the evaporative transpiration amount on the day I, Wseep is the amount of water seeping into the basement on the day i, and Qgw is the amount of groundwater returning to the stream on the day i.

#### 2.3.1. Data Input

In the implementation process, the SWAT method requires the import of basic physical data, including a digital elevation model (DEM) with elevation values between 90 to 1596 m (MSL). The watershed area has a slope from the west (mainly mountains and upstream forests) to the eastern lowland area where the Ubolratana Reservoir is located (see Figure 1). As for the soil type map (Figure 2a), it indicates that more than 50% of the soil is clay, which is in the eastern lowland, followed by clay loam soil, which is mainly in the eastern lowland of the study area. The types of land use in the study area were mostly agricultural areas. It was found that the use of land for rice farming which is most distributed in the eastern lowland area, combined with sugarcane and cassava plantation in the central area of the basin. In the west, most areas are watershed forests. The land use spatial distribution map is illustrated in Figure 2b.

**Figure 2.** Soil Type Map (**a**), Land use map (**b**).

Daily climate data includes rainfall, temperature, humidity, wind speed, and solar intensity. Daily rainfall data were collected from 9 rain gauge stations distributed in the study area and 1 climate station (Khon Kaen station) located in the southeastern part of the watershed, as shown in Figure 1. There are 4 stations of streamflow and sediment data, of which 3 stations are located in the watershed areas above the Ubolratana Reservoir, are Station E68A (Lam Pha Niang Basin), E29 Station (Upper Phong Basin), and E85 Station (Lam Chuan River). Basin). These data are from 2011–2019 supported by the Royal Irrigation Department of Thailand. The data used for evaluating the effectiveness of the SWAT-computed results for the different types, intervals, scales, and data sources used in this study are summarized and shown in Table 1.

**Table 1.** Basic data to be used in the SWAT model.

