Assessing Climate Change E ﬀ ects on Water Balance in a Monsoon Watershed

: Understanding the changes on future water resources resulting from climate variations will assist in developing e ﬀ ective management strategies for a river basin. Our area of interest is the Osan watershed in South Korea, where the summer monsoon contributes approximately 60–70% of the annual runo ﬀ and precipitation for the country. We determined the e ﬀ ects that future climatic changes have on this area. To accomplish this, we made use of global climate models (GCMs). A total of 10 GCMs were downscaled with the help of climate information production tools. Coupled with the GCMs and the Soil and Water Assessment (SWAT) model, three periods were used to assess these climate impacts. The baseline, mid-century (MC), and end-century (EC) periods include 1993–2018, 2046–2065, and 2081–2099, respectively. The entire process was performed using two scenarios (4.5 and 8.5) from the representative concentration pathways (RCPs). Some of the statistical metrics used for model calibration and validation were p-factor, r-factor, percent bias, root-mean-square error ( RMSE ), and Nash–Sutcli ﬀ e model e ﬃ ciency. Their respective values were 0.88, 0.88, 8.3, 0.91, and 0.91 for calibration, and 1.16, 0.85, 7.9, 0.88, and 0.87 for validation. For the MC and EC periods under both scenarios, we projected an increase in temperature and precipitation of approximately 2–5 ◦ C and 15–30%, respectively. A predicted rise in precipitation, surface ﬂow, lateral ﬂow, and water yield were noted for the month of June. Subsequently, a decline in July followed during the summer monsoon season. Summer monsoon rains will ﬂuctuate more sharply, with heavy rainfall in June, lower rainfall in July, and more rain in the late summer, leading to the possibility of both ﬂooding and drought within a given period. Annual precipitation, surface ﬂow, lateral ﬂow, and water yield will increase whereas evapotranspiration would decrease in both periods under both scenarios during the summer monsoon period, which will lead to wetter conditions in the future.


Introduction
Recurring changes in the climate have been predicted to detrimentally affect water resources on a global scale [1]. The water balance in river basins is known to be widely affected by human activities, and abrupt environmental and climatic changes [2]. In an attempt to document the effect of these changes, environmental organizations have developed climate models to project the consequences of future climate change based on representative concentration pathways (RCPs) for greenhouse gases extending to 2100. An example of such an organization is the World Climate Research Programme (WCRP), which has developed numerous climate models at a global scale (GCMs) under their Coupled Model Intercomparison Project 5 (CMIP5) program [3]. Using these GCMs to determine future changes in the water balance will aid in putting forward adaptation and management strategies to tackle the adverse effects of our changing climate on available water resources. water balance were investigated using 10 downscaled GCMs in the MC and EC periods in the Osan watershed under the RCP 4.5 and 8.5.

Study Area
The Osan stream watershed lies in central South Korea. The area of the watershed is 96.7 km 2 , with a total stream length of 16.49 km (Figure 1). This monsoon watershed has a precipitation of 1321 and a temperature of 12 °C per annum. Monsoon summers between June and September account for approximately 60-70% of the total runoff and precipitation and 30% for the other eight months [21,[35][36][37][38]. The mixed land use area is characterized by mountains and steep slopes.
The stream water is mostly used for irrigation. There is also a wastewater treatment plant (WWTP) on the stream with a capacity of about 140,000 m 3 d −1 and a pumping station located about 1 km from the plant [39]. The wastewater mixes with freshwater from the Osan stream and is used for irrigation further downstream. A streamflow gauging station is maintained by the Korean government.

SWAT Model
Recently, runoff-rainfall models have utilized the SWAT model across a variety of watersheds to evaluate the water balance and climatic changes [11]. It is an open-source, physical-based, and continuous-time model used in predicting subsurface and surface flow, sediment, and water quality in agricultural catchments in ungauged watersheds subject to various long-term land uses, and soil and management practices [40,41]. SWAT applications involve calibration and/or sensitivity assessment, climate change effects, analysis of the effect of variation in data input, and hydrological The stream water is mostly used for irrigation. There is also a wastewater treatment plant (WWTP) on the stream with a capacity of about 140,000 m 3 d −1 and a pumping station located about 1 km from the plant [39]. The wastewater mixes with freshwater from the Osan stream and is used for irrigation further downstream. A streamflow gauging station is maintained by the Korean government.

SWAT Model
Recently, runoff-rainfall models have utilized the SWAT model across a variety of watersheds to evaluate the water balance and climatic changes [11]. It is an open-source, physical-based, and continuous-time model used in predicting subsurface and surface flow, sediment, and water quality in agricultural catchments in ungauged watersheds subject to various long-term land uses, and soil and management practices [40,41]. SWAT applications involve calibration and/or sensitivity assessment, climate change effects, analysis of the effect of variation in data input, and hydrological and water quality analysis, all based on the combination of simplified processes of the upland and channel incorporated into the model [42]. The equation of water balance can be represented below as: where SW tot represents the total soil water content, and SW a denotes the initial soil water content for a given day i (mm H 2 O). P dy , F sur , E t , W p , and F gw are precipitation, surface flow, ET, return flow, and percolation (mm H 2 O), respectively. In our SWAT model, the Soil Conservation Service (SCS) Curve Number (CN) method [43], the Penman-Monteith method [44][45][46], and the variable storage channel routing method [47] were employed. Previous studies provide an extensive summary on carrying out the SWAT model [11,48].
The model demarcated the watershed into sub-basins, and threshold values for the soil, slope, and land use were set at 0% when defining the hydrological response units (HRUs) for the watershed. In order to maintain each landscape feature, a threshold of 0% was maintained in the model representation [50][51][52]. The model established 247 HRUs for 35 sub-basins, which was in accordance The model demarcated the watershed into sub-basins, and threshold values for the soil, slope, and land use were set at 0% when defining the hydrological response units (HRUs) for the watershed. In order to maintain each landscape feature, a threshold of 0% was maintained in the model representation [50][51][52]. The model established 247 HRUs for 35 sub-basins, which was in accordance with the ratio range of 1-10 for HRUs and sub-basins suggested by the SWAT manual [53].

SWAT Model Evaluation
The SWAT calibration and uncertainty program (SWAT-CUP) software package provides a framework for decision-making integrating a semi-automated technique made up of sensitivity, calibration, and uncertainty analyses [54]. Observed monthly flow and simulated flow data from ArcSWAT were the model inputs. Generalized likelihood uncertainty estimation determined the sensitive parameters in SWAT-CUP [55] that were dependent on 25 established parameters for streamflow [11,56]. The model was calibrated using trial and error in comparing simulated and monthly observations until the model simulations satisfactorily matched the measured data [57][58][59].
Six objective functions were used in calibration and validation. Uncertainty analysis in SWAT-CUP was employed by the Sequential Uncertainty Fitting Version 2 (SUFI-2) method. The average output uncertainty of the Latin hypercube sample was determined by the 95 percent prediction uncertainty (95PPU) by the cumulative distribution rate at 2.5% and 97.5%. The p-factor is the 95PPU band proportion and the r-factor is a measure of the 95PPU band thickness. A simulation is deemed to correlate with the observed data at a point where the r-factor is nearest to zero and the p-factor is nearest to one. Theoretically, p-factors range between 0% and 100%, while r-factors range from 0 to infinity. SUFI-2 attempts to keep all of the data (i.e., a large p-factor, maximum of 100%) while minimizing the r-factor (minimum of 0). SUFI-2 first estimates parameters with a large variance within a theoretically justifiable range in order for the calculated data to immediately fall within 95PPU. This variance is subsequently reduced in stages while the p-factor and r-factor are tracked. Details are available in the manual [55,[60][61][62].

Downscaling and Bias Correction of Future Climate Data
Watershed models and GCMs can be used in examining climate change impacts on watershed hydrology. However, GCMs include major uncertainty, and the Intergovernmental Panel on Climate Change (IPCC; 2007) advises that the effects of multiple models and projections be included in research on climate change [68]. Statistical downscaling methods are thus essential for the long-term reliability of observed data sets representing base climate data. In the present study, precipitation (ppt), minimum temperature (Tmin), and maximum temperature (Tmax) were downscaled using observed data from 1993 to 2018. Typically, RCPs [69] and Special Report on Emissions Scenarios (SRES) [70] generate a variety of viable emission scenarios for climate projections.
The climate models in CMIP5 operate under four RCPs that provide daily climate projections (RCP 2.6, 4.5, 6.0, and 8.5) up to EC [71]. A total of 20 GCMs that included daily ppt, Tmax, and Tmin were selected by Eun and Cannon [72] and compared based on their ability to replicate extreme climate indices. The comparison was ranked by a group of experts (Expert Team on Climate Change Detection and Indices, ETCCDI) at the World Meteorological Organization [73]. Based on this finding, we selected the top 10 GCMs (Table 2). We used the RCP4.5 and RCP 8.5 scenarios. RCP 4.5 and 8.5 were selected because they represent a stable-end and a high-end climate change projection. These scenarios provide a wide range of long-term outcomes. RCPs do not have particular climate policy actions, hence they are suitable for use to explore a wide range of long-term outcomes. The RCP4.5 scenario assumes a world using technologies and strategies leading to stabilized radiative forcing before 2100 at 4.5 W/m 2 . Contrariwise, in the RCP8.5 scenario, high population growth and a lack of highly developed technologies leads to radiative forcing reaching a high level, that is, 8.5 W/m 2 in 2100 [69]. For this downscaling process, a climate information production toolkit developed by the APEC climate center APCC Integrated Model Solution (AIMS) was used. This toolkit uses CMIP5 and CORDEX raw climate data for the downscaling of 29 GCMs for RCP 4.5 and 8.5 [74,75]. Depending on the project of interest, a user can either select a climate change or seasonal forecasting option in the AIMS software. The two data sources available for the project (CMIP5 and CORDEX) are coupled with the region of interest. Observed data is uploaded and evaluated with weather variables (e.g., ppt, Tmax and Tmin). GCMs models can be selected by the user to be downscaled for the observed, historical, and future periods under both scenarios. The selection of downscaling methods (SQM, SDQDM, and BCSA) using ETCCDI indices was also required [73]. In our study, a climate change project was run with the following ecosystem: water objective, CIMP5 data source, South Korea, observed data from the KMA, and SDQDM downscaling for RCP 4.5 and 8.5. Spatial correlations in temperature and precipitation to an acceptable degree were produced by the model [76]. Details of the QDM and SDQDM processes can be found in [72,77]. Details of the AIMS software are also available in its user manual, which can be freely downloaded (https://aims.apcc21.org/). Daily ppt, Tmax, and Tmin data from 1976-2018 were used as the historical period and the 2006-2099 period data were available for future projections. The baseline, MC, and EC periods include 1993-2018, 2046-2065, and 2081-2099, respectively. Past research has reported average atmospheric CO 2 concentrations of 330, 540, and 940 ppm for the base period, RCP 4.5, and RCP 8.5, respectively, in the MC and EC periods [78][79][80]. To ensure that the model ran smoothly, a five-year warm-up period was established. A SWAT weather generator aided in simulating missing and unavailable data for wind velocity, solar radiation, and humidity for future projections [54]. Land cover was assumed not to change significantly as this has been addressed in other studies [10,14].

Model Evaluation
The calibration process aimed at minimizing variations among simulated and observed values. Five years (2010-2014) of monthly streamflow data were utilized for calibrating and four years (2015-2018) of data for validating the model. In the SWAT-CUP, parameters that were highly sensitive were identified for calibration using generalized likelihood uncertainty estimation. CN, ALPHA_BF, GW_DELAY, CH_K2, and SOL_AWC were the sensitive parameters as they directly affected streamflow ( Table 3). The parameter identifier indicates the type of change made in order to calibrate the model whereas the range was the limit in which the parameters were adjusted in SWATCUP. The fitted value was achieved using trial and error until there was a good match between the simulated and observed values.
The shaded green regions in Figure 3 indicate the 95PPU. The p-and r-factors were both 0.88 in the calibration and 0.85 and 1.16 in the validation, respectively ( The shaded green regions in Figure 3 indicate the 95PPU. The p-and r-factors were both 0.88 in the calibration and 0.85 and 1.16 in the validation, respectively (

Projected Precipitation and Temperature
Precipitation increases were projected in the Osan watershed for the 10 GCMs used in the study. The average increase from the baseline period for all the GCMs was 0.51 mm (15.6%) in the MC period and 0.71 mm (21.7%) in the EC period under RCP 4.5 (Figure 4). RCP 8.5 predicted an increase of 0.67 (20%) and 1 mm (30.6%) in the MC and EC periods. Under RCP 4.5, precipitation exhibited the largest increase in HadGEM2-ES in both century periods, while the smallest increase was observed for CMCC-CM in the MC period and INM-CM4 in the EC period (Figure 5a). Under RCP 8.5, the highest increase was observed in MRI-CGCM3 in the MC period and CNRM-CM5 in the EC period, while the smallest increase was projected by INM-CM4 for both periods (Figure 5b).  Temperature and precipitation in the Osan watershed will increase by approximately 2-5 °C and 15-30% in the MC and EC periods under both scenarios. Reports from the National Institute of Meteorological Research in 2009 predicted that temperatures will rise by 4 °C and precipitation will increase by 17% by 2100 [3], which is in close conformity with the projected results of this study. An increase in temperature may lead to longer cultivation periods and crop yields may also decrease due to water stress or a shift in water availability. Precipitation amounts greatly affect water resources and an increase in precipitation may lead to increased water spillage and possible flooding scenarios. Understanding these changes will aid decision makers to have a picture of the future scenarios, which may help in future climate change adaptation strategies.  Temperature and precipitation in the Osan watershed will increase by approximately 2-5 °C and 15-30% in the MC and EC periods under both scenarios. Reports from the National Institute of Meteorological Research in 2009 predicted that temperatures will rise by 4 °C and precipitation will increase by 17% by 2100 [3], which is in close conformity with the projected results of this study. An increase in temperature may lead to longer cultivation periods and crop yields may also decrease due to water stress or a shift in water availability. Precipitation amounts greatly affect water resources and an increase in precipitation may lead to increased water spillage and possible flooding scenarios. Understanding these changes will aid decision makers to have a picture of the future scenarios, which may help in future climate change adaptation strategies.

Monthly Climate Change Impact on Water Balance
Under RCP 4.5 and RCP 8.5 in the MC and EC periods from the baseline period, the future monthly water balance was also investigated for the Osan watershed. Figure 6 shows histograms and standard error bars for the water balance variable. Here, data uncertainty is clumped around the mean for all the simulated variables. Figure 6a shows that precipitation would increase from the baseline period in most months under both scenarios. Precipitation would decline in the MC period in January, February, and July and in the EC in February and July under RCP 4.5. Precipitation would also decrease in February and July for the MC period whereas only in July for the EC period under RCP 8.5. The largest increase in precipitation under RCP 4.5 was in June, with increases of 72.84 (57%) and 88 South Korea's monsoons normally occur between June and August, delivering about 60-70% of the annual total rainfall. The results for both periods indicated a significant rise in precipitation in June and a slight decrease in July under both scenarios. Fluctuations of the precipitation increase in June and decrease in July may be due to a shift in the monsoon season as it starts earlier than usual. The summer monsoon in Korea has two peaks in sub seasonal rainfall structures from late June to mid-July and from mid-August to early September [81]. High atmospheric CO2 concentrations in the RCP4.5 and RCP8.5 scenario (540 and 940 ppm for both mid-century and end-century compared to 330 ppm for baseline) used in the study may be the main reason for the changes as atmospheric CO2 is expected to increase in the future. This may cause floods in June and droughts in July, thus affecting rain-fed crops and possibly leading to the destruction of property because 80% of Korea consists of hills [27]. A shift in future precipitation is likely to affect agriculture because the planting and harvesting periods may change.
Surface flow also increased in most months for both periods under both scenarios (Figure 6b). Under RCP 4.5, the surface flow decreased in January, February, July, and August for the MC period and in January, February, and July for the EC period. Under RCP 8.5, surface flow decreased in Temperature and precipitation in the Osan watershed will increase by approximately 2-5 • C and 15-30% in the MC and EC periods under both scenarios. Reports from the National Institute of Meteorological Research in 2009 predicted that temperatures will rise by 4 • C and precipitation will increase by 17% by 2100 [3], which is in close conformity with the projected results of this study. An increase in temperature may lead to longer cultivation periods and crop yields may also decrease due to water stress or a shift in water availability. Precipitation amounts greatly affect water resources and an increase in precipitation may lead to increased water spillage and possible flooding scenarios. Understanding these changes will aid decision makers to have a picture of the future scenarios, which may help in future climate change adaptation strategies.

Monthly Climate Change Impact on Water Balance
Under RCP 4.5 and RCP 8.5 in the MC and EC periods from the baseline period, the future monthly water balance was also investigated for the Osan watershed. Figure 6 shows histograms and standard error bars for the water balance variable. Here, data uncertainty is clumped around the mean for all the simulated variables. Figure 6a shows that precipitation would increase from the baseline period in most months under both scenarios. Precipitation would decline in the MC period in January, February, and July and in the EC in February and July under RCP 4.5. Precipitation would also decrease in February and July for the MC period whereas only in July for the EC period under RCP 8. 5 South Korea's monsoons normally occur between June and August, delivering about 60-70% of the annual total rainfall. The results for both periods indicated a significant rise in precipitation in June and a slight decrease in July under both scenarios. Fluctuations of the precipitation increase in June and decrease in July may be due to a shift in the monsoon season as it starts earlier than usual. The summer monsoon in Korea has two peaks in sub seasonal rainfall structures from late June to mid-July and from mid-August to early September [81]. High atmospheric CO 2 concentrations in the RCP4.5 and RCP8.5 scenario (540 and 940 ppm for both mid-century and end-century compared to 330 ppm for baseline) used in the study may be the main reason for the changes as atmospheric CO 2 is expected to increase in the future. This may cause floods in June and droughts in July, thus affecting rain-fed crops and possibly leading to the destruction of property because 80% of Korea consists of hills [27]. A shift in future precipitation is likely to affect agriculture because the planting and harvesting periods may change.
Surface flow also increased in most months for both periods under both scenarios (Figure 6b). Under RCP 4.5, the surface flow decreased in January, February, July, and August for the MC period and in January, February, and July for the EC period. Under RCP 8.5, surface flow decreased in January, February, and July for the MC period and only in January for the EC period. The largest increase in surface flow under RCP 4.5 was in June with 53.19 (117.3%) and 59.89 mm (132%) in the MC and EC periods. Under RCP 8.5, the largest increase occurred in June: 67.41 (148.6%) and 87.51 mm (192.9%) for the MC and EC periods, respectively. The largest surface flow decrease under RCP 4.5 was 21.09 (10.5%) and 12.95 mm (6.4%) for July in the MC and EC periods, while the largest decrease under RCP 8.5 was in July in the MC period (11.09 mm; 5.5%) and January in the EC period (1.13 mm; 23.4%). Because precipitation increased and decreased in June and July, respectively, surface runoff also increased and decreased in the same months because surface runoff depends on precipitation. Because precipitation increased and decreased in June and July, respectively, surface runoff also increased and decreased in the same months because surface runoff depends on precipitation.
Lateral flow exhibited a similar pattern to the other water balance variables, increasing in most months for both periods under both scenarios (Figure 6c). Total lateral flow was projected to decrease in May and August in both periods under RCP 4.5 but increase under RCP 8.5. The highest lateral flow increase under RCP 4.5 was in June, with 0.6 mm (43%) for the MC period and 0.89 mm (63.5%) in the EC; under RCP 8.5, June also exhibited the highest increase, with 0.96 (68.6%) and 1.13 mm (81%), respectively. The largest decrease in lateral flow under RCP 4.5 was 0.12 mm (7.2%) for the MC period and 0.04 mm (2%) for the EC period in May; under RCP 8.5, the largest decrease was 0.03 (1.9%) and 0.28 mm (16%), respectively, in the same month.
Water yield was predicted to decrease in February and July under RCP 4.5, whereas it would rise under RCP 8.5 for both periods. The largest increase in the water yield under RCP 4.5 was in June, with 54.13 (100.6%) and 62.41 mm (115.9%) for the MC and EC periods, respectively. June also saw the largest rise in water yield under RCP 8.5 (72.4 [134.5%] and 96.1 mm [178.5%], respectively). The largest decrease in the water yield from the baseline period under RCP 4.5 was 14.59 mm (6.6%) for the MC period and 4.24 mm (1.9%) for the EC period, both in July. The smallest increase in water yield under RCP 8.5 was in February, with 0.74 mm (9.6%) in the MC period and 4.4 mm (57.4%) in the EC period (Figure 6d).
ET was predicted to decrease from June to November and slightly fluctuate in the other months for both periods under both RCP scenarios (Figure 6e). The largest increase in ET was in June under RCP 4.5 (6 mm [8.1%] for the MC period and 9.98 mm [13.4%] for the MC period). The largest increase in ET under RCP 8.5 was in June for the MC period (2.1 mm; 2.7%) and in October for the EC period (0.38 mm; 1.2%). The largest decrease in ET under RCP 4.5 was in August, with 13.6 (12.4%) and 13.41 mm (12.2%) in the EC periods, respectively. The smallest increase in ET under RCP 8.5 was also in August, with 24.16 (22%) and 25.94 mm (23.6%), respectively. The larger ET decrease in July-October was due to decreases in transpiration caused by an increased atmospheric CO2 concentration. Plant activities play a major role in ET during this period when plants are actively developing [82].

Impact of Climate Change on Annual Water Balance
Results from the SWAT model for the annual water balance under the RCP 4.5 and 8.5 scenarios for the MC and EC periods are shown on Table 5. These results would be of use in projecting future water availability within the Osan watershed. Precipitation would increase for both periods under both scenarios: 6.2% in the MC and 12.2% in the EC periods under RCP 4.5 and 10.7% for the MC and 23.4% for the EC periods under RCP 8.5. Similarly, the surface flow would increase for both periods and both scenarios. Under RCP 4.5, the surface flow increased by 13.4% and 22.0% and, under RCP 8.5, it increased by 22.9% and 49.0% in the MC and EC periods, respectively.
Water yield within the Osan watershed also increased in all cases: 13.6% in the MC period and 22.7% in the EC period under RCP 4.5 and 27.5% in the MC period and 55.6% in the EC period under RCP 8.5. Lateral flow would increase for both periods and both scenarios: 7.2% and 13.6% for the MC and EC periods, respectively, under RCP 4.5 and 16.0% and 29.7% for the MC and EC periods, respectively, under RCP 8.5.
ET was predicted to decrease from baseline for both periods and scenarios. ET decreased by 2.2% and 0.1% in the MC and EC periods, respectively, under RCP 4.5 and by 8.5% and 13.6% in the MC and EC periods, respectively, under RCP 8.5. This may be due to the high atmospheric CO2 concentrations used in the RCP 4.5 and 8.5 scenarios (540 ppm for the MC and 940 ppm for the EC periods with respect to 330 ppm as the baseline). Transpiration decreases as the atmospheric CO2 concentration increases due to plants having efficient water use [10,[83][84][85]. On the other hand, evaporation depends mainly on temperature and water availability.
The results thus revealed that precipitation, surface flow, lateral flow, and water yield would increase while ET would decrease in the Osan watershed in the MC and EC periods for both RCPs 4.5 and 8.5. This is expected as a result of an increase in precipitation in the region; thus, as precipitation increases, ET decreases, and as precipitation decreases, ET increases. The differences in precipitation and temperature that would drive more ET are outweighed by atmospheric CO2 concentration changes. Hence, a large decrease in transpiration will result in a decrease in ET. Transpiration plays a key role in the water cycle, especially in our area of study, which is predominantly made up of cultivated crops and forest cover [10]. These results are similar to a Ministry of Environment report in 2018, which predicted the average precipitation in seven major domestic areas compared to the past three decades , projecting a 14.8% increase in the early 21st century (2011-2040), a 17.1% increase by the mid-21st century (2071-2100), and an 11.4% increase by the late 21st century (2071-2100) [86]. In accordance with these projections, floods and The largest decrease in the water yield from the baseline period under RCP 4.5 was 14.59 mm (6.6%) for the MC period and 4.24 mm (1.9%) for the EC period, both in July. The smallest increase in water yield under RCP 8.5 was in February, with 0.74 mm (9.6%) in the MC period and 4.4 mm (57.4%) in the EC period (Figure 6d).
ET was predicted to decrease from June to November and slightly fluctuate in the other months for both periods under both RCP scenarios (Figure 6e). The largest increase in ET was in June under RCP 4.5 (6 mm [8.1%] for the MC period and 9.98 mm [13.4%] for the MC period). The largest increase in ET under RCP 8.5 was in June for the MC period (2.1 mm; 2.7%) and in October for the EC period (0.38 mm; 1.2%). The largest decrease in ET under RCP 4.5 was in August, with 13.6 (12.4%) and 13.41 mm (12.2%) in the EC periods, respectively. The smallest increase in ET under RCP 8.5 was also in August, with 24.16 (22%) and 25.94 mm (23.6%), respectively. The larger ET decrease in July-October was due to decreases in transpiration caused by an increased atmospheric CO 2 concentration. Plant activities play a major role in ET during this period when plants are actively developing [82].

Impact of Climate Change on Annual Water Balance
Results from the SWAT model for the annual water balance under the RCP 4.5 and 8.5 scenarios for the MC and EC periods are shown on Table 5. These results would be of use in projecting future water availability within the Osan watershed. Precipitation would increase for both periods under both scenarios: 6.2% in the MC and 12.2% in the EC periods under RCP 4.5 and 10.7% for the MC and 23.4% for the EC periods under RCP 8.5. Similarly, the surface flow would increase for both periods and both scenarios. Under RCP 4.5, the surface flow increased by 13.4% and 22.0% and, under RCP 8.5, it increased by 22.9% and 49.0% in the MC and EC periods, respectively. ET was predicted to decrease from baseline for both periods and scenarios. ET decreased by 2.2% and 0.1% in the MC and EC periods, respectively, under RCP 4.5 and by 8.5% and 13.6% in the MC and EC periods, respectively, under RCP 8.5. This may be due to the high atmospheric CO 2 concentrations used in the RCP 4.5 and 8.5 scenarios (540 ppm for the MC and 940 ppm for the EC periods with respect to 330 ppm as the baseline). Transpiration decreases as the atmospheric CO 2 concentration increases due to plants having efficient water use [10,[83][84][85]. On the other hand, evaporation depends mainly on temperature and water availability.
The results thus revealed that precipitation, surface flow, lateral flow, and water yield would increase while ET would decrease in the Osan watershed in the MC and EC periods for both RCPs 4.5 and 8.5. This is expected as a result of an increase in precipitation in the region; thus, as precipitation increases, ET decreases, and as precipitation decreases, ET increases. The differences in precipitation and temperature that would drive more ET are outweighed by atmospheric CO 2 concentration changes. Hence, a large decrease in transpiration will result in a decrease in ET. Transpiration plays a key role in the water cycle, especially in our area of study, which is predominantly made up of cultivated crops and forest cover [10]. These results are similar to a Ministry of Environment report in 2018, which predicted the average precipitation in seven major domestic areas compared to the past three decades , projecting a 14.8% increase in the early 21st century (2011-2040), a 17.1% increase by the mid-21st century (2071-2100), and an 11.4% increase by the late 21st century (2071-2100) [86]. In accordance with these projections, floods and other weather-related disasters were estimated to occur more frequently [87,88]. Due to an increase in precipitation, flooding will also increase the surface flow, thus raising the risk of landslides and affecting water leakage and water recharge. Climate change will likely influence water quality as a rise in water temperature would affect water ecosystems [89]. In combatting the negative climate change effects on water resources, adaptation strategies are required, such as dam construction and improved dam/reservoir operating methods, in the region [90,91].

GCMs Variability
Investigating the differences in the water balance predicted by the individual GCMs under the RCP 4.5 and 8.5 scenarios for the MC and EC periods are essential for identifying the degree to which changes in climate would influence water availability in the Osan watershed (Table 6). Under RCP 4.5, precipitation increased within all GCMs for both periods except for CMCC-CM in the MC period, which predicted a considerable decline. In addition, water yield and surface water were expected to increase as the precipitation increased. Water yield and surface water increased for both periods for all GCMs with the exceptions of CMCC-CM and MRI-CGCM3, which predicted a decrease in the MC period. Lateral flow increased in all GCMs in both periods except for CMCC-CM. ET decreased in all GCMs in both periods except for CMCC-CM and CCSM4, which predicted 1.5% and 1.3% increases in the MC period and 3.2% and 0.6% increases in the EC period.
Under RCP 8.5, precipitation increased within all GCMs for both periods with the exception of MRI-CGCM3, which decreased by 0.5%. Surface flow also increased in all GCMs for both periods, with the largest increase of 49.7% observed in CNRM-CM5 in the MC period and 101.7% in HadGEM2-ES for the EC period. In all periods, water yield increased in all GCMs, with the largest increase in CNRM-CM5 in the MC period (52.2%) and in HadGEM2-ES in the EC period (105.6%). Lateral flow increased in all GCMs for both periods except a decrease of 0.3% in CMCC-CM in the MC period. ET decreased for all GCMs for both periods, with the largest decrease of 15% for MRI-CGCM3 in the MC and of 20.2% for HadGEM2-ES in the EC period. The only exception was a slight increase of 2.9% for CMCC-CM in the MC period.

Conclusions
Future climate change impacts on the water balance were assessed using the SWAT model in the Osan watershed, South Korea. On a daily time-step, the model was calibrated using six statistic metrics. Model calibration and validation were satisfactory. Ten GCMs were used to predict these impacts from 2046-2065 (MC) and 2081-2099 (EC) periods from a baseline period of 1993-2018 under RCPs 4.5 and 8.5.
We projected hotter and wetter future conditions using the 10 downscaled GCMs. This occurs as a result of an increase in temperature and precipitation for both MC and EC under the RCP 4.5 and 8.5 scenarios when compared to the baseline. Monthly precipitation, surface flow, lateral flow, and water yield will rise in the month of June and a decline in July during the summer monsoon season in the future. The atmospheric CO 2 concentration increase leads to a decrease in ET for the MC and EC period under the RCP 4.5 and 8.5 scenarios. This may be a result of a higher atmospheric moisture demand due to an increase in temperature. Similarly, annual precipitation, surface flow, lateral flow, and water yield was projected under RCP 4.5 and 8.5 for the two periods. These increases would significantly affect the water balance in the Osan watershed, where storms and floods may occur with greater frequency. Annual ET was expected to decline in the MC and EC period under both scenarios. This is expected as a result of an increase in precipitation; thus, as precipitation increases, ET decreases and vice versa. These findings were consistent with other studies in the region.
Overall, the Osan watershed will experience wetter conditions in the future, which may make it vulnerable to floods if not adequately managed. Summer monsoon rains may fluctuate more sharply, with heavy rains in June, lower rainfall in July, and more rain in the late summer, leading to the possibility of both flooding and drought in the future. This would provide a major challenge in the management and planning of water resources.
In the present study, we did not consider potential land-use changes or human-related activities. Further research in this regard is necessary for decision-makers to efficiently manage the water resources within the watershed. However, the study's results improve the understanding of climate change impacts on the Osan watershed under various climate change scenarios in the future.