Mitigating Future Water Scarcity Through Comprehensive Assessment of Climate and Socio-Environmental Impacts in River Basins

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The MRB is crucial for agriculture and domestic use but faces vulnerability due to climate change and societal factors. The current understanding of future water deficits in the MRB is limited, necessitating a comprehensive assessment. The research aims to evaluate the effects of socio-environmental changes on water supply and demand. Results show that strategic interventions like high conveyance efficiency and moderate Alternate Wetting and Drying techniques can mitigate unmet water demand caused by population growth and additional water users until 2080. However, climate change and forest loss are projected to exacerbate water scarcity, especially in agricultural regions dependent on water resources. Model simulations demonstrate the WEAP model's reliability in predicting streamflow. These findings underscore the need for targeted interventions and highlight the effects of climate change and forest loss on water resource management. The study recommends implementing high conveyance efficiency and moderate Alternate Wetting and Drying techniques to alleviate water scarcity and promote resilience, advancing integrated water resources planning and policy analysis. Water resource management Water supply and demand Water Evaluation and Planning (WEAP) Water scarcity Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Climate change is expected to influence natural weather system variability and watershed hydrology. Also, land degradation will adversely affect the availability of water since forest loss will inevitably result in soil erosion in watersheds and sedimentation in reservoirs, thus reducing their storage capacities. Water consumption rises with population growth, and climate change is expected to increase agricultural water demand. The rice production areas account for a large portion of the water demand in the Magat River Water (MRB). Hence, it is critical to implement irrigation technologies in these production areas to save water and increase water supply availability. Recent studies (Felipe, A.J. et al., 2023) highlight within the framework of ongoing global temperature rise, there is a projected increase of up to 30% in the occurrence and severity of extreme weather events, including droughts due to the impact of climate change. This increase in susceptibility to droughts is particularly relevant in the Magat River Basin, underscoring the urgent need for strategies addressing both mitigation and adaptation. Heavy rainfall attributed to climate change also has significant effects on the hydrological dynamics of the Magat River Basin, as explored by Singson, C.L. et al. in their study published in 2023. The Research indicates that the inflow of the basin is predicted to experience an increase of up to 20% during periods of heightened precipitation, such as wet years. Concurrently, land degradation, driven in part by deforestation, amplifies the strain on water availability. There is no evident erosion observed in the low-lying areas of the watershed, however, the southern and mountainous portion of the river basin observed severe erosion accounting for 27.4% (Elazegui, D.D. and Combalicer, E.A. 2004). Furthermore, escalating water consumption due to population growth compounds the issue, while climate change-driven shifts in precipitation patterns intensify agricultural water demand. Particularly concerning is the rice production sector, which accounts for a substantial portion of water usage in regions like the Magat River Basin. In this context, adopting advanced irrigation technologies becomes an imperative. Implementing efficient irrigation practices has the potential not only to conserve water but also to enhance water resource availability, playing a crucial role in addressing the escalating water crisis. Therefore, it is important to quantify the possible climate change impact on water supply and demand in the Magat River Basin. The significance of hydrological models is paramount in analyzing the complex interplay between climate and water resources (Olsson et al., 2017 ). Researchers worldwide have harnessed various hydrological models to assess water resources. Noteworthy examples include the Soil and Water Assessment Tool (SWAT) watershed model developed by Arnold and Forher (2005) which is well-suited for large watersheds and complex terrain and integrates hydrological, agricultural, and water quality processes, however, it requires extensive data for calibration and validation and complexity may lead to challenges in model setup and interpretation. The Water Resources Management Model (WRMM) proposed by Cutlac and Horbulyk ( 2011 ) which emphasizes water allocation and management strategies, but it relies on accurate data for various sectors, which may be limited. The Modular Simulator (ModSim) devised by Heidari ( 2018 ) is suitable for integrated water resources management, but the model setup and data collection may be time consuming and the complex interactions between modules might require expertise for accurate simulation. And the widely used Water Evaluation and Planning Tool (WEAP), applied in diverse basins globally (Asghar et al., 2019 ), further exemplify the versatile landscape of hydrological modeling, a user-friendly interface and wide adoption for integrated water resources planning and it incorporates socio-economic factors and environmental considerations. The SEI developed the WEAP System model to facilitate the assessment of planning and management concerns linked to water resources development. This versatile model is applicable to both municipal and agricultural systems, addressing various issues such as sectoral demand analyses, water conservation, water rights, allocation priorities, streamflow simulation, reservoir operation, ecosystem requirements, and project cost-benefit analyses (SEI 2001). Some studies have used the Water Evaluation and Assessment Planning (WEAP) model to address water resource management challenges, it's vital to highlight research conducted in the Philippines, a region with unique water-related issues. For example, Schneider and colleagues ( 2019 ) investigated how using the Alternate Wetting and Drying (AWD) technique in irrigated rice production in Central Luzon could reduce water requirements and enhance water availability. However, our study differs in focus, specifically delving into the Magat River Basin (MRB). Our aim is to evaluate the hydrological impacts of water-saving techniques, considering the influences of changing climate patterns and socio-environmental factors. While both studies contribute valuable insights into water resource management in the Philippines, they address different aspects of the challenge. Our study seeks to provide a more comprehensive assessment by considering a broader range of factors influencing water scarcity in the region. Research in the Magat River Basin has identified critical gaps in understanding the combined impacts of climate change, land degradation, and increasing water consumption. These gaps pertain to the projected increase in extreme weather events, complex rainfall patterns, land degradation as anticipated by deforestation, and escalating water demand. Addressing these challenges is imperative, emphasizing the need for advanced irrigation technologies. This study employed the WEAP system model to assess quantitatively the possible impact of climate change on water supply and demand in the Magat River Basin, specifically evaluating the effectiveness of advanced irrigation technologies in mitigating water scarcity and enhancing water resource availability. 2 Methodology 2.1 Study area The Magat River Basin has been chosen as the optimal location for this study. Designated as a forest reservation area through Proclamation 573 on June 26, 1969 (Elazegui & Combalicer, 2004 ), it holds critical watershed status in region 2 due to its role in maintaining ecological balance and its economic significance to the local population and the Philippines at large. Situated in the northern part of the Philippines, it spans major portions of Nueva Vizcaya, and parts of Quirino and Isabela provinces in the Cagayan Valley region, Philippines covering a total area of 5,156 square kilometers. The climate within the Magat watershed is categorized as Type I and Type III according to Corona's classification. The western section falls under Type I climate, characterized by a dry season from December to May and a wet season from June to November. This section is exposed to the Southwest Monsoon, receiving a substantial amount of rainfall brought by tropical cyclones occurring from June to September. The eastern section, on the other hand, experiences a Type III climate. The type III climate season is characterized by a relatively dry period from November to April, but there is no pronounced maximum rain period (Tattao, 2010 ). 2.2 Description of the WEAP model The Water Evaluation and Planning (WEAP) tool is a user-friendly application that employs a comprehensive approach to water resource planning and policy analysis. Utilizing the water balance principle to simulate hydrological processes (Asghar et al., 2019 ), this model demonstrates its versatility across individual river basins and complex basin systems, as seen in the study of Metobwa et al. in 2018. Offering a thorough evaluation of factors including hydrology, land use, hydrogeology, climate, water quality, and water allocation, the WEAP model functions as a conceptual model, enabling the representation of the physical system, as outlined by Li et al. in 2015. Previously, the WEAP model has been proven successful in applications to agricultural and urban catchments worldwide in particular, simulating climate (Joyce et al., 2005 ; Mehta et al., 2013 ; Ougougdal et al., 2020 ), water supply and demands (Yao et al., 2021 ; Agarwal et al., 2018 ), and population growth (Arsiso et al., 2017 ). 2.3 Model development This water supply and demand study generally followed the methodological framework shown in Fig. 1 . The data on water resources which include hydrologic variables and water demand were gathered and used as inputs in the WEAP model. Considering this local data, the WEAP model was calibrated and validated until it satisfactorily mimics the hydrological processes in the Magat River watersheds. The simulation of scenarios considering socio-economic, technological, climate changes, and forest loss was done to assess its impacts on water supply and demand in the area. Furthermore, the hydrological model is semi-theoretical, continuous, semi-distributed, and deterministic. Given its semi-theoretical nature, the model requires calibration and verification. Notably, the WEAP system does not offer automatic calibration for the hydrological model, necessitating a manual implementation of the calibration process. The standard method calculates water demand by multiplying the activity level with the water use rate across various sectors, including industrial, municipal, and agricultural. This method applies to all sectors except for agriculture. To allocate resources among demands, WEAP utilizes a one-period linear programming routine, as outlined by Letcher et al. (2007) and Van Cauwenbergh et al. ( 2008 ). 2.4 Model calibration and validation The parameters governing runoff generation from climate inputs underwent calibration and validation using historical streamflow observations from gauging stations in the Magat River Basin. Additionally, the calibration parameters of the WEAP model were manually adjusted, drawing on data collected, existing literature, and expert knowledge. Model performance evaluation utilized statistical indices, including the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS). 2.5 Data input and data collection To simulate monthly hydrological processes in the WEAP hydrological model, meteorological/climate data are essential. This includes monthly information on precipitation, temperature, wind speed, and relative humidity, alongside Digital Elevation Model (DEM), land use, and streamflow data. The DEM and land use data were procured from the National Mapping and Resources Information Authority (NAMRIA). Meanwhile, climate and streamflow data were obtained from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) and the National Irrigation Administration (NIA), respectively. Demand-related data, encompassing population water use rates, water consumption, agricultural demand, and population growth rates, were sourced from the National Water Resources Board (NWRB) and the Philippine Statistics Authority (PSA). 2.6 Schematic description of Magat sub-basin WEAP model The Magat River Basin WEAP model structure is divided into seven (7) hydrological catchments, namely Alimit, Subwatershed 2, Ibulao, Lamut, Subwatershed 1, Matuno, and Sta. Fe with corresponding percent areas of 14.82%, 10.83%, 15.13%, 7.64%, 14.61%, 14.70%, and 22.28% in the Magat Watershed, respectively. Specifically, there is one (1) municipality that may have been considered domestic, while one (1) is accounted for agricultural water use along the downstream of the Magat River Basin. Figure 2 shows the schematic illustration of the model at Magat River Basin. 2.7 Future scenarios development Watershed-based water management represents a collaborative strategy that brings together all water stakeholders to formulate planned, concerted, and consensus-driven actions for the common good. This participatory approach involves individuals from diverse backgrounds, even when their interests may diverge. The WEAP system facilitates the creation of different current scenarios and their simulation in the future, addressing various "what if" questions, as discussed by Sieber & Purkey in 2015. These simulations can be compared with the existing situation. In addition to the reference scenario, four scenarios were developed for this study: the inclusion of additional Local Government Units (LGUs) as water users, changes in population growth rate, adoption of new irrigation techniques, and consideration of climate change. These scenarios aim to predict the future gap between water supply and demand. The identified management intervention options are designed to meet both current and future water demands at a minimal cost. The following section outlines the four scenarios developed: Baseline scenario (BS). The initial baseline scenario developed represents the present water supply and demand conditions within the watershed. This scenario forms the foundation for a more in-depth analysis of the current state and for making comparisons with other simulated scenarios. The data from the current accounts, covering the years 2000 to 2020, serves as the foundational information for the model. From this, various scenarios are created to investigate possible changes in the system in the years beyond the current accounts year, including 2025, 2030, 2050, and 2080, effectively extending the assessment of the current situation into the future. Furthermore, the domestic water demand for the baseline year was based on the actual population of the LGU who tapped Magat River as a source of their domestic water. The water users in the reference scenario are the current water users of the water resources in the basin which include the NIA-Magat River Integrated Irrigation System service areas and the LGU of Alfonso Lista. Also, the industry, mining, and other LGUs were added in the reference scenario as water users of the groundwater resources. Additional LGUs as water users. In this set of scenarios, the purpose of the Magat Dam for domestic water use was extended to LGU Santiago City outside the Magat River watershed. The baseline scenario for domestic water use is LGU-Alfonso Lista in Ifugao. Population growth scenario. Under this scenario, the population growth was projected using the arithmetic method for 2025, 2030, 2050, and 2080. The population growth rate in Region 2 from 2000 to 2021 was 3.23 percent (PSA, 2020). There are four scenarios under this category, namely 2025, 2030, 2050, and 2080 population. The baseline scenario is the current domestic water usage of Alfonso Lista. Irrigation system improvement scenario. The primary livelihood for the people in the Magat watershed is agriculture, but this fragile ecosystem is experiencing growing degradation due to a combination of several factors, including adverse climate changes that are already impacting local farmers. The impact of increased conveyance efficiency because of irrigation system improvement was evaluated at 95 percent. According to the Food and Agriculture Organization (FAO 1989), paving canals with concrete would achieve a conveyance efficiency of 95 percent. This technique allows for the improvement of conveyance efficiency due to the improvement measures. According to Alejo and Balderama ( 2021 ), the actual average conveyance efficiency of Magat is 76 percent. The water users from a certain LGU, specifically for irrigation and domestic purposes, are the same across scenarios. This is to allow assessment of the impact of improved conveyance efficiencies on water demand and unmet water demand. There were three scenarios used under conveyance efficiency, namely, 95 percent conveyance efficiency which is categorized as high, conveyance efficiency of 87 percent as moderate, and 80 percent conveyance efficiency as low. The alternate wetting and drying, with assumed water savings of 15 percent (low), 25 percent (moderate), and 30 percent (high) based on literature and the NIA Master Plan for 2020–2030, was also simulated under the irrigation system improvement scenario. Climate change scenario. By using the Climate Information Risk Analysis Matrix (CLIRAM) tool, PAGASA was able to provide the downscaled projected changes in climate variables, particularly for rainfall and temperature in both mid (2036–2065) and late 21st century (2070–2099). Table 1 as shown below quantifies the projections on the rainfall and mean temperature. These were inputted into the WEAP model after it was considered acceptable. Table 1 Projected changes in seasonal Rainfall and mean temperature mid (2036–2065) and late (2070–2099) 21st century. Projected Changes in Seasonal Rainfall in the Mid-21st Century (2036–2065). Month Moderate Emission (RCP4.5) High Emission (RCP8.5) Lower Bound Median Bound Upper Bound Lower Bound Median Bound Upper Bound DJF 3.5 11.5 48.4 -1.5 12.4 34.8 MAM 1.1 10.3 23.5 1.1 7 17.3 JJA -27.7 -17.2 0.3 -24.2 -2.6 25.7 SON -4.4 3 11.9 -2.3 11 16.2 Projected Changes in Seasonal Mean Temperature in the Mid-21st Century (2036–2065). Month Moderate Emission (RCP4.5) High Emission (RCP8.5) Lower Bound Median Bound Upper Bound Lower Bound Median Bound Upper Bound DJF 1 1.2 1.5 1.1 1.6 1.8 MAM 0.9 1.2 1.7 1.2 1.7 2.3 JJA 1 1.3 2 1.3 1.6 2.5 SON 1 1.1 1.9 1.3 1.6 2.3 Projected Changes in Seasonal Rainfall in the Late-21st Century (2070–2099). Month Moderate Emission (RCP4.5) High Emission (RCP8.5) Lower Bound Median Bound Upper Bound Lower Bound Median Bound Upper Bound DJF 0.2 16.5 41.7 -10.2 32.3 51.1 MAM -11.2 2.4 27.3 -6.7 9.7 28.4 JJA -26.8 -12.9 6.2 -31.2 -21.6 15.2 SON -5.2 4.4 12.2 -5.1 5.7 24.7 Projected Changes in Seasonal Mean Temperature in the Late-21st Century (2070–2099). Month Moderate Emission (RCP4.5) High Emission (RCP8.5) Lower Bound Median Bound Upper Bound Lower Bound Median Bound Upper Bound DJF 1.1 1.5 2.2 1.9 2.9 3.5 MAM 1.2 1.7 2.6 2.4 2.9 4 JJA 1.4 1.6 2.7 2.8 3.3 4.5 SON 1.3 1.5 2.7 2.6 3.1 4.3 Forest loss scenario. The land use change scenario suggests a notable increase in agricultural land in the basin. This change can be attributed to the decrease in grassland which was converted into agricultural land. The forested land increased from 90,516.48 ha to 170,490.93 ha from 2006 to 2010. This may be attributed to the reforestation initiatives, people-participated reforestation, and afforestation led by NIA-MARIIS DRD. The urban areas also increased five-fold, possibly due to the increase of urbanization in the provinces where the watershed is located. Likewise, the water bodies in the basin are also seen expanding from 5,629.39 ha to 9,415.75 ha. Based on the land-use change from 2010 to 2015, there is an increase in agricultural land by 21 percent due to the conversion of grasslands and forested lands into agricultural areas. On the other hand, the forested lands and water bodies of the basin are observed to be declining. Both areas have a 13 percent decrease from 2010 to 2015. There are three scenarios under this category, namely, 2030, 2050, and 2080 forest loss scenario. 3 Results and discussions 3.1 Model calibration and validation The WEAP model calibration and validation over the Magat River Basin were performed using the streamflow data from the Magat River Gauge Station. The monthly streamflow recorded at the station was compared against the simulated values of the model from 2000–2015 for the calibration period and 2016–2020 for the validation period. The model performance was evaluated by calculating the coefficient of determination (R 2 ), the Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) for both calibration and validation periods. The efficiency of Nash-Sutcliffe varied from -∞ to 1 as stated by Gupta et al., in 2011. According to Ritter & Muñoz-Carpena, in 2013, the effectiveness of 1 simply means that the modeled flow corresponds perfectly to the observed data. In addition, the simulated monthly streamflow matches the observed values satisfactory with NSE of 0.64 and R 2 = 0.74 for the calibration period, and NSE = 0.77 and R 2 = 0.83 for the validation period (Fig. 3 ). In terms of PBIAS, it was estimated at -8.44 and 0.85 for the calibration and validation periods, respectively. This implies that the WEAP model accurately simulated the streamflow in the Magat watershed and can be a useful tool for modeling the impact of climate changes in the Philippine watersheds. The calibrated parameters used in the WEAP model for the Magat watershed is shown in Table 2 . Actually, there are nine influential parameters calibrated in the WEAP model using manual calibration. These parameters directly influence the streamflow along the Magat watershed. These parameters are preferred flow direction, Kc, root zone conductivity, runoff resistance factor, soil water capacity, deep water capacity, initial Z1, and initial Z2. The preferred flow direction, Kc, and Runoff resistance factor are sensitive to streamflow. Table 2 Calibrated Parameters Used in the WEAP Model for the Magat Watershed. Parameters Default Values Optimized Values Preferred Flow Direction, (0 and Higher) 0.15 0.1 Kc, (0 and Higher) 1 10.80 to 10.95 Root Zone Conductivity, (0.1 and Higher) 20 0.76 to 201.12 Runoff Resistance Factor, (0 to 1000) 2 2.99 to 358.16 Soil Water Capacity, (0 and Higher) 1000 531.36 to 594.68 Initial Z1, (0 to 100%) 30% 35 Deep Conductivity, (0.1 and Higher) 20 15 Deep Water Capacity, (0 and Higher) 1000 300 Initial Z2, (0 to 100%) 30% 50 3.2 Baseline scenario Baseline scenario depicts the current situation of water demand and the availability in the watershed. It can be used to do a thorough analysis of the current situation to compare it with other simulated possibilities. The combined domestic and agriculture surface water has the highest water supply from the river, which is equal to 5431.11 million cubic meters (MCM), while the industry, mining, and domestic groundwater are 1485.85 MCM. Since the Magat Dam only covers surface water, it has a total water supply of 3,006.02 MCM for domestic and agricultural purposes. The domestic surface water has a water demand of 3.108 MCM and an unmet water demand of 0.747 MCM. On the other hand, agricultural surface water has a water demand of 3,217.547 MCM and an unmet water demand of 213.888 MCM. Additionally, the water demand for the industry, mining, and domestic groundwater has a corresponding value of 4.536 MCM, 114.510 MCM, and 115.011 MCM, respectively. 3.3 Most likely case scenario Water scarcity is exacerbated by population increase. Population increase reduces per capita water supply and drives people to nearby areas and cities already experiencing water shortages. Under this scenario, the population growth rate scenarios used are the baseline scenario and the increase in population growth rate. The model's base year is set as the current year (2020), and all the inputs were loaded into WEAP from which the results were generated. Figure 4 illustrates the simulation results for the water demand (domestic surface water across population growth scenarios. For Scenario 1 (2025 population), results show a 9.46 percent increase in water demand relative to the baseline scenario. The results further indicate a slight increase in water demand for the 2025 population. Secondly, there is a percentage increase of 18.93 percent for Scenario 2 (2030 population) and 56.78 percent for Scenario 3 (2050 population). By 2080 (Scenario 4), the results show a percentage increase of 113.57 percent. Hence, the water demand for Scenario 4 is very high relative to the baseline scenario. The assumed growth rate per scenario was projected using an arithmetic method. Population growth is a significant cause of water scarcity. It contributes to the increase in demand for irrigation water. Given that the supply from the dam is enough to meet the projected water demand across population growth scenarios, improvement of irrigation systems is needed to address the unmet water demand. The impact of increased transport efficiency on water demand and unmet water demand was compared using two parameters: a) in terms of water Conveyance Efficiency (CE) where low (80%), moderate (87%), or high (95%) is a result of canal improvement which decreases the conveyance losses; and b) in terms of alternate wetting and drying (AWD) where low (15%), moderate (25%), and high (30%) of water is being saved decreasing the demand by 15, 25 and 30 percent which decreases the application losses. 3.3.1 Low CE and low AWD Based on Table 3 , because of the combined low CE and low AWD interventions, the unmet water demand for domestic surface water by 2025 will decrease by 32.57 percent relative to the baseline. In 2030 and 2050, the unmet water demand will further decrease by 28.26 percent and 3.06 percent, respectively. On the other hand, in the year 2080, the unmet water demand will increase by 38.61 percent. In comparison, the impact of the combined low CE and low AWD interventions on unmet water demand for irrigation (agricultural surface water) will decrease at an average of 86.42 percent in all the timeframes of the simulation. 3.4 Impact of improving conveyance efficiency and alternative wetting and drying interventions on unmet water demand across population growth 3.4.1 Moderate CE and low AWD Since the combined interventions of low CE and low AWD are not enough to supply the demand for water from 2025 to 2080 both for domestic and agricultural surface water, the combination of moderate CE and low AWD interventions was used to evaluate the unmet water demand for the projected population growth. As a result, the unmet water demand for agricultural surface water is zero in all the timeframes (Table 3 ), this means that the water savings from the combination of moderate CE and low AWD interventions are enough to meet the water demand in agricultural surface water (NIA-MARIIS service area) from 2025 to 2080. However, there is still unmet water demand on the domestic water surface. The unmet water demand for domestic surface water will decrease by 37.73 percent, 33.66 percent, and 14.68 percent for the scenarios 2025, 2030, and 2050, respectively, relative to the baseline scenario. In the 2080 scenario, the unmet water demand for domestic surface water will increase by 32.58 percent compared to the baseline. This means that the combination of moderate CE and low AWD interventions is still not enough to meet the water demand for domestic surface water. 3.4.2 Moderate CE and moderate AWD Since the combined interventions of moderate CE and low AWD are still not enough to supply the demand for water from 2025 to 2080 for domestic surface water, the combination of moderate CE and moderate AWD interventions was used to evaluate the water and unmet water demand for population growth. Table 3 presents the unmet water demand for domestic surface water will significantly decrease by 65.67 percent, 55.72 percent, and 25.75 percent for the 2025, 2030, and 2050 scenarios, respectively. In the 2080 scenario, the unmet water demand will increase by 22.18 percent relative to the baseline. This means that the combination of moderate CE and moderate AWD interventions is still not enough to supply the demand for domestic surface water. 3.4.3 High CE and low AWD As tabulated in Table 3 , the impact of high CE and low AWD interventions on unmet water demand for domestic surface water across population growth since the combination of moderate CE and moderate AWD is still not enough to supply the needed water demand. The results show that there is a significant decrease of up to 74.41 percent for 2025, 74.36 percent for 2030, 63.72 percent for 2050, and 45.36 percent for the 2080 scenarios. With still a percentage of unmet water demand, this means that the combination of these interventions is still not enough to supply the water demand for domestic surface water. 3.4.4. High CE and moderate AWD The impact of high CE and moderate AWD will give a zero unmet water demand both for the domestic and agricultural surface water across all population growth scenarios. This means that 100% of the water demand for domestic and agricultural surface water is met at any time based on the simulations (Table 3 ). Table 3 The unmet water demand percentage increases and decreases for domestic and agricultural water users. Scenarios Unmet Water Demand Domestic Surface Water, cu. m. Increase /Decrease (%) Agriculture Surface Water, cu. m. Increase /Decrease (%) Reference Scenario Baseline 747,402 213,887,744 2025 813,493 8.84% 213,966,864 0.04% 2030 879,512 17.68% 214,045,984 0.07% 2050 1,153,643 54.35% 214,336,096 0.21% 2080 1,603,598 114.56% 214,758,064 0.41% Most Likely Scenarios (Low CE and Low AWD) Baseline 747,402 213,887,744 2025 503,958 (32.57%) 28,651,990 (86.60%) 2030 536,181 (28.26%) 28,736,386 (86.56%) 2050 724,556 (3.06%) 29,116,162 (86.39%) 2080 1,035,970 38.61% 29,664,728 (86.13%) Moderate CE and Low AWD Baseline 747,402 213,887,744 2025 465,405 (37.73%) 0 *No more unmet 2030 495,820 (33.66%) 0 * No more unmet 2050 637,674 (14.68%) 0 * No more unmet 2080 990,905 32.58% 0 * No more unmet Moderate CE and Moderate AWD Baseline 747,402 213,887,744 2025 256,549 (65.67%) 0 * No more unmet 2030 330,954 (55.72%) 0 * No more unmet 2050 554,940 (25.75%) 0 * No more unmet 2080 913,189 22.18% 0 * No more unmet High CE and Low AWD Baseline 747,402 213,887,744 2025 191,241 (74.41%) 0 * No more unmet 2030 191,651 (74.36%) 0 * No more unmet 2050 271,193 (63.72%) 0 * No more unmet 2080 408,373 (45.36%) 0 * No more unmet High CE and Moderate AWD Baseline 747,402 213,887,744 2025 0 * No more unmet 0 * No more unmet 2030 0 * No more unmet 0 * No more unmet 2050 0 * No more unmet 0 * No more unmet 2080 0 * No more unmet 0 * No more unmet 3.5 Impact of the conveyance efficiency and alternate wetting and drying on unmet water demand with Santiago City as additional water users across population growth scenarios Now, what if Santiago City will tap water from the Magat River, obviously water demand will increase. To measure its impact, the combined high CE and moderate AWD interventions were used to evaluate the unmet water demand since this combined scenario was shown in earlier simulations as enough to supply water both for domestic and agricultural surface water. Simulation results show that water demand will increase by as high as 473.61 percent or 17.83 million cu m for the year 2025, 511.00 percent (18.99 million cu m) in 2030, 660.56 percent (23.64 million cu m) in 2050, and 884.91 percent (30.61 million cu m) in 2080 as shown in Fig. 5 . Despite the huge increase in water demand for domestic in terms of population growth and with the addition of Santiago City as a water user, the water supply is still enough to supply water by 2025, 2030, 2050, and 2080. As seen in Fig. 6 , the impact of the combination of high CE and moderate AWD interventions on unmet water demand for both domestic and agricultural surface water is now zero across all scenarios with Santiago City as an additional water user. 3.6 Impacts of climate change on agricultural and domestic water user Future climate change is projected to increase the variability of temperature and precipitation, which could significantly impact the agricultural sector. In this study, the average unmet water demand for agriculture and domestic purposes under the baseline scenario was estimated to be 388.28 MCM using the WEAP model. From the baseline scenario, the model predicts an increase in the future unmet water demand during the dry years of the mid and late 21st century for both Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios, while a decrease is projected during the normal and wet years. During the dry years, unmet water demand will increase in the mid-21st century from 99.97 percent (429.21 MCM) to 117.34 percent (466.48 MCM) for RCP 4.5 and RCP 8.5, respectively. Likewise, in the late 21st century, there will be an estimated increase from 97.80 percent (424.55 MCM) to 108.68 percent (447.90 MCM). During this period, water demands are at the highest level while the water supply is low. Furthermore, rising temperatures are expected to hasten the evaporation of water and land surfaces and accelerate transpiration, thus, an increase in the unmet water demand is projected. Furthermore, during the normal and wet years, the unmet water demand is estimated to increase ranging from 61.68 to 83.46 percent and 15.68 to 27.38 percent, respectively. The water supply and demand for the climate change scenario are shown in Table 4 . The table reveals that the water supply accessible from the dam is at its peak during wet years across all the defined climate change scenarios. However, the water supply will not be sufficient to meet the future water demand. Correspondingly, unmet water demand is projected to be at its highest during the dry years. Table 4 Water supply and demand for climate change scenarios. Climate Change Scenario Water Supply Accessible from the Dam, MCM Water Demand, MCM Unmet Water Demand, MCM Mid-21st century (2036–2065) - RCP 4.5 Dry years 2,791.45 3,220.65 429.21 Normal years 2,826,89 3,220.65 393.77 Wet years 2,947.26 3,220.65 273.39 Mid-21st century (2036–2065) - RCP 8.5 Dry years 2,754.17 3,220.65 466.48 Normal years 2,833.62 3,220.65 387.04 Wet years 2,954.68 3,220.65 265.97 Late 21st century (2070–2099) - RCP 4.5 Dry years 2,796.11 3,220.65 424.55 Normal years 2,847.98 3,220.65 372.68 Wet years 2,972.36 3,220.65 248.3 Late 21st century (2070–2099) - RCP 8.5 Dry years 2,772.76 3,220.65 447.9 Normal years 2,873.63 3,220.65 347.02 Wet years 2,967.43 3,220.65 253.22 3.7 Forest loss scenarios Figure 7 illustrates the simulation results of unmet water demand in the forest loss scenario. Specifically, it shows an increase of 99.3 percent with a value of 426.21 MCM in Scenario 1 (25% forest loss for 2030) relative to the baseline. Both Scenario 2 (36% forest loss for 2050) and Scenario 3 (53% forest loss for 2080) have a percentage increase of 99.7 percent and 100.1 percent, and corresponding values of 427.11 MCM and 427.99 MCM, respectively. 4 Conclusion The impact of climate change, land degradation, and increasing water consumption pertains to the projected increase in extreme weather events, complex rainfall patterns, land degradation due to deforestation, and escalating water demand. Addressing these challenges is imperative, emphasizing the need for advanced irrigation technologies. The study employed the WEAP model to assess the impact of various factors on water demand and unmet water demand in the Magat River Basin. Population growth is projected to increase water demand significantly in the coming decades, while low CE and low AWD interventions are shown to reduce unmet water demand, particularly in the short term, with a slight increase in 2080. High CE and moderate AWD interventions eliminate unmet water demand across all population growth scenarios. However, the addition of Santiago City as water user increases demand substantially, necessitating the implementation of high CE and moderate AWD interventions to meet this demand. Climate change is expected to exacerbate future unmet water demand, emphasizing the importance of intervention measures. Although the water supply is theoretically sufficient, accessibility issues and increasing demands pose challenges, highlighting the need for immediate implementation of high CE and moderate AWD interventions to improve water availability. Declarations Declaration of competing interest The authors declare that there are no competing interests that could have appeared to influence the work in this research paper. Funding The authors declare that no funds, grants, or any other support were received during the preparation of this research paper. Data availability All the relevant data will be made available upon request. Acknowledgment This project was made possible through the support of the National Economic Development Authority Region 02 for funding and with the cooperation of the DRD NIA MARIIS Division, Ramon, Isabela for providing the data used in the calibration of the model. Author Contribution All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by E.R., L.A., O.B., JL.B., C.B., A.A., and C.A. The first draft of the manuscript was written by E.R., and all authors commented on previous versions of this study. All authors read and approved this study. References Agarwal, S., Patil, J. P., Goyal, V. C., & Singh, A. (2018). Assessment of water supply–demand using Water Evaluation and Planning (WEAP) model for Ur River Watershed, Madhya Pradesh, India. Journal of The Institution of Engineers (India) , Series A, 100(1) , 21-32. https://doi.org/10.1007/s40030-018-0329-0. Alejo, L. A., & Balderama, O. F. (2021). Impacts of management and modernization on water savings in large irrigation systems. Journal of Biodiversity and Environmental Sciences , 18 (5), 35-42. https://innspub.net/jbes/impacts-of-management-and-modernization-on-water-savings-in-large-irrigation-systems/. Arnold, J.G.; Fohrer, N. SWAT2000: Current Capabilities and Research Opportunities in Applied Watershed Modelling. Hydrol. Process. 2005, 19 , 563–572. [CrossRef]. Arsiso, K. B., Tsidu, G. M., Stoffberg, G. H., & Tadesse, T. (2017). Climate change and population growth impacts on surface water supply and demand of Addis Ababa, Ethiopia. Climate Risk Management , 18 , 21-33. http://dx.doi.org/10.1016/j.crm.2017.08.004. Asghar, A., Iqbal, J., Amin, A., & Ribbe, L. (2019). Integrated hydrological modeling for assessment of water demand and supply under socio-economic and IPCC climate change scenarios using WEAP in Central Indus Basin. Journal of Water Supply: Research and Technology - AQUA , 68 (2), 136-148. https://doi.org/10.2166/aqua.2019.106. Cutlac, I.M.; Horbulyk, T.M. optimal water allocation under short-run water scarcity in the South Saskatchewan River Basin. J. Water Resour. Plan. Manag. 2011, 137 , 92–100. [CrossRef]. Elazegui, D., & Combalicer, E. (2004). Realities of the watershed management approach: The Magat Watershed experience. Philippines Institute for Development Studies . https://dirp3.pids.gov.ph/ris/dps/pidsdps0421.pdf. Felipe, A.J., Alejo, A., Balderama, O., Rosete, E. (2023). Climate Change Intensifies the Drought Vulnerability of River Basins: A Case of the Magat River Basin. Journal of Water and Climate Change. Vol. 00. No. 0, 1 DOI: 10.2166/wcc.2023.005. Food and Agriculture Organization (FAO). (1989). Irrigation water management: Irrigation scheduling . Gupta, H. V., & Kling, H. (2011). On typical range, sensitivity, and normalization of mean squared error and Nash-Sutcliffe Efficiency type metrics. Water Resources Research , 47 (10). https://doi.org/10.1029/2011WR010962. Heidari, A. Application of multidisciplinary water resources planning tools for two of the largest rivers of Iran. J. Appl. Water Eng. Res. 2018, 6 , 150–161. [CrossRef]. Joyce, B., Vicuña, S., Dale, L., Dracup, J., Hanemann, M., Purkey, D., & Schwarzenegger, A. (2005). Climate change impacts on water for agriculture in California: A case study in the Sacramento Valley. Leong, W. K., & Lai, S. H. (2017). Application of water evaluation and planning model for integrated water resources management: Case Study of Langat River Basin, Malaysia. IOP Conference Series: Materials Science and Engineering , 210 (1), 12-24. https://doi.org/10.1088/1757-899X/210/1/012024. Mehta, V. K., Haden, V. R., Joyce, B. A., Purkey, D. R., & Jackson, L. E. (2013). Irrigation demand and supply, given projections of climate and land-use change in Yolo County, California. Agricultural Water Management , 117 , 70-82. 10.1016/j.a gwat.2012.10.021. Metobwa, M. O., Maurad, A. K., & Ribbe, L. (2018). Water demand simulation using WEAP 21: A case study of the Mara River Basin, Kenya. International Journal of Natural Resource Ecology and Management , 3 (1), 9-18. https://doi.org/10.11648/j.ijnrem.20180301.12. National Water Resources Board. (n.d.). Home . Retrieved November 30, 2021, from https://nwrb.gov.ph/. Olsson, T.; Kämäräinen, M.; Santos, D.; Seitola, T.; Tuomenvirta, H.; Haavisto, R.; Lavado-Casimiro, W. Downscaling climate projections for the Peruvian coastal Chancay-Huaral Basin to support river discharge modeling with WEAP. J. Hydrol. Reg. Stud. 2017, 13 , 26–42. [CrossRef]. Ougougdal, H. A., Khebiza, M. Y., Messouli, M., & Lachir, A. (2020). Assessment of future water demand and supply under IPCC climate change and socio-economic scenarios, using a combination of models in Ourika Watershed, High Atlas, Morocco. MPDI Water , 12 , 1751. https://doi.org/10.3390/w12061751. PAGASA. (n.d.). Climate Projections . PAGASA. Retrieved November 29, 2021, from https://www1.pagasa.dost.gov.ph/index.php/93-cad1/472-climate-projections#maincontents Philippine Statistics Authority (PSA). (2020). Country’s total water abstraction increased by 12.3 percent from 2010 to 2019 [Press Release]. https://psa.gov.ph/sites/default/files/attachments/ird/specialrelease/1.%20Press%20Release.pdf. Ritter, A., & Muñoz-Carpena, R. (2013). Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments. Journal of Hydrology , 480 , 33-45. https://doi.org/10.1016/j.jhydrol.2012.12.004. Sieber, J., & Purkey, D. (2015). Water evaluation and planning system user guide . Stockholm Environment Institute. Retrieved March 12, 2020, from https://www.weap21.org/downloads/WEAP_User_ Guide.pdf Schneider P, Sander BO, Wassmann R, Asch F. 2019. Potential and versatility of WEAP model (Water Evaluation and Planning System) for hydrological assessments of AWD (Alternate Wetting and Drying) in irrigated rice. Agricultural Water Management 224:105559. Singson, C., Alejo, L., Balderama, O., Bareng, J.L., Kantoush, S. (2023). Modeling Climate Change Impact on the Inflow of the Magat Reservoir using the Soil and Water Assessment Tool (SWAT) Model for Dam Management. Journal of Water & Climate Change. 14(3): 633-650. https://doi.org/10.2166/wcc.2023.240. Tattao, E. (2010). The use of GIS and remote sensing in the assessment of Magat watershed in the Philippines [Master's Thesis]. Massey University. Van Cauwenbergh, N., Pinte, D., Tilmant, A., Frances, I., Pulido-Bosch, A., & Vanclooster, M. (2008). Multi-objective, multiple participant decision support for water management in the Andarax catchment, Almeria. Environmental Geology , 54 (3), 479-489 https://ui.adsabs.harvard.edu/link_gateway/2008EnGeo..54..479V/doi:10.1007/s00254-007-0847-y. Yao, A. B., Mangoua, O., Georges, E. S., Kane, A., & Goula, B. (2021). Using “Water Evaluation and Planning” (WEAP) model to simulate water demand in Lobo watershed (Central-Western Cote d’Ivoire). Journal of Water Resource and Protection , 13 , 216-235. https://doi.org/10.4236/jwarp.2021.133013. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 May, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 20 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 18 Dec, 2024 Reviewers agreed at journal 17 Dec, 2024 Reviewers agreed at journal 01 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 30 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers invited by journal 26 Apr, 2024 Submission checks completed at journal 12 Apr, 2024 Editor assigned by journal 12 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4250146","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290376128,"identity":"feccb5c6-6930-45b5-a0c4-f4df785a6965","order_by":0,"name":"Elmer Rosete","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACdlSuDQ+YApEGuLQwI3MOMKSRruUwA0Et/MzciY8rGOwYzNvbHz7+UHFeRn5GAuODt20MeeY4tEg28242PMOQzCBz5oyxwYEzt3kMbiQwG85tYyi2bMCuxeAw7zbJBqDzJCRy2CQOtgG1SCSwSfO2MSRuOIBdi/1h3u0/GxjqgVrSn/84+O8cD9Bh7L/xaTFg5t3G2AD0tYREghnDwYYDPAw3EtiY8WmROMy7WbLB4DiPBM8ZY4kzx5J5DM48bJacc06i2ACHFv723o0fGyqq5STY2x9+qKixs5dvTz744U2ZTR4uLVDnMfAg8YAOBVqfgE8DdkCGllEwCkbBKBimAADgelQwenKYVwAAAABJRU5ErkJggg==","orcid":"","institution":"Isabela State University","correspondingAuthor":true,"prefix":"","firstName":"Elmer","middleName":"","lastName":"Rosete","suffix":""},{"id":290376132,"identity":"0a6129bf-2312-4283-b4de-83ad4bb506fa","order_by":1,"name":"Lanie Alejo","email":"","orcid":"","institution":"Isabela State 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04:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4250146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4250146/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-025-05583-z","type":"published","date":"2025-05-31T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54812856,"identity":"db9909c0-fe46-46b7-ab62-abe5d82af83d","added_by":"auto","created_at":"2024-04-17 06:39:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":392282,"visible":true,"origin":"","legend":"\u003cp\u003eWEAP Model Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/e0c84ba8130dc7e8c021e1a2.png"},{"id":54812252,"identity":"847f1cbe-0b35-40f5-a9ea-e4efb9e77595","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111556,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Illustration of the Magat River Basin in WEAP Application.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/48060b1577905c64f25f53d3.png"},{"id":54812254,"identity":"2dfdc6fb-7c69-41f3-b6b8-d72c101daa7e","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86989,"visible":true,"origin":"","legend":"\u003cp\u003eWEAP Model Calibration and Validation of the Magat Watershed.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/76b950b8cf362cf482f1dbad.png"},{"id":54812253,"identity":"db49a808-f176-46d3-a334-6f6a39349f5a","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53483,"visible":true,"origin":"","legend":"\u003cp\u003eWater Demand (domestic surface water) across population growth scenarios.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/c2f57548a7e2a2df987d40bc.png"},{"id":54812257,"identity":"03a5999e-36d6-47a0-a67a-30fb42bcb938","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24196,"visible":true,"origin":"","legend":"\u003cp\u003eWater demand (domestic surface water) with Santiago City as additional water users across population growth scenarios.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/e99b5a634f81b3486b238766.png"},{"id":54812258,"identity":"09ccfcb5-81e7-49b9-b9c7-6225b7552c72","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":19601,"visible":true,"origin":"","legend":"\u003cp\u003eImpacts of High CE and Moderate AWD interventions on unmet water demand (domestic and agricultural surface water) with Santiago City as additional water users across population growth scenarios.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/fe94f67da614e0b733d6ec29.png"},{"id":54812255,"identity":"c05fa969-49f4-46ed-9519-87bb25665574","added_by":"auto","created_at":"2024-04-17 06:31:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25939,"visible":true,"origin":"","legend":"\u003cp\u003eUnmet water demand for the forest loss scenario.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/27bb5cbf9e0e27db070f882e.png"},{"id":83783111,"identity":"b702c5c4-08b3-421d-a788-64cc551380e0","added_by":"auto","created_at":"2025-06-02 16:10:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2003827,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4250146/v1/444901fb-1e15-4357-86a6-74db9fc736e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitigating Future Water Scarcity Through Comprehensive Assessment of Climate and Socio-Environmental Impacts in River Basins","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change is expected to influence natural weather system variability and watershed hydrology. Also, land degradation will adversely affect the availability of water since forest loss will inevitably result in soil erosion in watersheds and sedimentation in reservoirs, thus reducing their storage capacities. Water consumption rises with population growth, and climate change is expected to increase agricultural water demand. The rice production areas account for a large portion of the water demand in the Magat River Water (MRB). Hence, it is critical to implement irrigation technologies in these production areas to save water and increase water supply availability.\u003c/p\u003e \u003cp\u003eRecent studies (Felipe, A.J. et al., 2023) highlight within the framework of ongoing global temperature rise, there is a projected increase of up to 30% in the occurrence and severity of extreme weather events, including droughts due to the impact of climate change. This increase in susceptibility to droughts is particularly relevant in the Magat River Basin, underscoring the urgent need for strategies addressing both mitigation and adaptation. Heavy rainfall attributed to climate change also has significant effects on the hydrological dynamics of the Magat River Basin, as explored by Singson, C.L. et al. in their study published in 2023. The Research indicates that the inflow of the basin is predicted to experience an increase of up to 20% during periods of heightened precipitation, such as wet years. Concurrently, land degradation, driven in part by deforestation, amplifies the strain on water availability. There is no evident erosion observed in the low-lying areas of the watershed, however, the southern and mountainous portion of the river basin observed severe erosion accounting for 27.4% (Elazegui, D.D. and Combalicer, E.A. 2004). Furthermore, escalating water consumption due to population growth compounds the issue, while climate change-driven shifts in precipitation patterns intensify agricultural water demand. Particularly concerning is the rice production sector, which accounts for a substantial portion of water usage in regions like the Magat River Basin. In this context, adopting advanced irrigation technologies becomes an imperative. Implementing efficient irrigation practices has the potential not only to conserve water but also to enhance water resource availability, playing a crucial role in addressing the escalating water crisis. Therefore, it is important to quantify the possible climate change impact on water supply and demand in the Magat River Basin.\u003c/p\u003e \u003cp\u003eThe significance of hydrological models is paramount in analyzing the complex interplay between climate and water resources (Olsson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Researchers worldwide have harnessed various hydrological models to assess water resources. Noteworthy examples include the Soil and Water Assessment Tool (SWAT) watershed model developed by Arnold and Forher (2005) which is well-suited for large watersheds and complex terrain and integrates hydrological, agricultural, and water quality processes, however, it requires extensive data for calibration and validation and complexity may lead to challenges in model setup and interpretation. The Water Resources Management Model (WRMM) proposed by Cutlac and Horbulyk (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) which emphasizes water allocation and management strategies, but it relies on accurate data for various sectors, which may be limited. The Modular Simulator (ModSim) devised by Heidari (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) is suitable for integrated water resources management, but the model setup and data collection may be time consuming and the complex interactions between modules might require expertise for accurate simulation. And the widely used Water Evaluation and Planning Tool (WEAP), applied in diverse basins globally (Asghar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), further exemplify the versatile landscape of hydrological modeling, a user-friendly interface and wide adoption for integrated water resources planning and it incorporates socio-economic factors and environmental considerations.\u003c/p\u003e \u003cp\u003eThe SEI developed the WEAP System model to facilitate the assessment of planning and management concerns linked to water resources development. This versatile model is applicable to both municipal and agricultural systems, addressing various issues such as sectoral demand analyses, water conservation, water rights, allocation priorities, streamflow simulation, reservoir operation, ecosystem requirements, and project cost-benefit analyses (SEI 2001). Some studies have used the Water Evaluation and Assessment Planning (WEAP) model to address water resource management challenges, it's vital to highlight research conducted in the Philippines, a region with unique water-related issues. For example, Schneider and colleagues (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) investigated how using the Alternate Wetting and Drying (AWD) technique in irrigated rice production in Central Luzon could reduce water requirements and enhance water availability. However, our study differs in focus, specifically delving into the Magat River Basin (MRB). Our aim is to evaluate the hydrological impacts of water-saving techniques, considering the influences of changing climate patterns and socio-environmental factors. While both studies contribute valuable insights into water resource management in the Philippines, they address different aspects of the challenge. Our study seeks to provide a more comprehensive assessment by considering a broader range of factors influencing water scarcity in the region.\u003c/p\u003e \u003cp\u003eResearch in the Magat River Basin has identified critical gaps in understanding the combined impacts of climate change, land degradation, and increasing water consumption. These gaps pertain to the projected increase in extreme weather events, complex rainfall patterns, land degradation as anticipated by deforestation, and escalating water demand. Addressing these challenges is imperative, emphasizing the need for advanced irrigation technologies.\u003c/p\u003e \u003cp\u003eThis study employed the WEAP system model to assess quantitatively the possible impact of climate change on water supply and demand in the Magat River Basin, specifically evaluating the effectiveness of advanced irrigation technologies in mitigating water scarcity and enhancing water resource availability.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Magat River Basin has been chosen as the optimal location for this study. Designated as a forest reservation area through Proclamation 573 on June 26, 1969 (Elazegui \u0026amp; Combalicer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), it holds critical watershed status in region 2 due to its role in maintaining ecological balance and its economic significance to the local population and the Philippines at large. Situated in the northern part of the Philippines, it spans major portions of Nueva Vizcaya, and parts of Quirino and Isabela provinces in the Cagayan Valley region, Philippines covering a total area of 5,156 square kilometers.\u003c/p\u003e \u003cp\u003eThe climate within the Magat watershed is categorized as Type I and Type III according to Corona's classification. The western section falls under Type I climate, characterized by a dry season from December to May and a wet season from June to November. This section is exposed to the Southwest Monsoon, receiving a substantial amount of rainfall brought by tropical cyclones occurring from June to September. The eastern section, on the other hand, experiences a Type III climate. The type III climate season is characterized by a relatively dry period from November to April, but there is no pronounced maximum rain period (Tattao, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Description of the WEAP model\u003c/h2\u003e \u003cp\u003eThe Water Evaluation and Planning (WEAP) tool is a user-friendly application that employs a comprehensive approach to water resource planning and policy analysis. Utilizing the water balance principle to simulate hydrological processes (Asghar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), this model demonstrates its versatility across individual river basins and complex basin systems, as seen in the study of Metobwa et al. in 2018. Offering a thorough evaluation of factors including hydrology, land use, hydrogeology, climate, water quality, and water allocation, the WEAP model functions as a conceptual model, enabling the representation of the physical system, as outlined by Li et al. in 2015.\u003c/p\u003e \u003cp\u003ePreviously, the WEAP model has been proven successful in applications to agricultural and urban catchments worldwide in particular, simulating climate (Joyce et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Mehta et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ougougdal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), water supply and demands (Yao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Agarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and population growth (Arsiso et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Model development\u003c/h2\u003e \u003cp\u003eThis water supply and demand study generally followed the methodological framework shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The data on water resources which include hydrologic variables and water demand were gathered and used as inputs in the WEAP model. Considering this local data, the WEAP model was calibrated and validated until it satisfactorily mimics the hydrological processes in the Magat River watersheds. The simulation of scenarios considering socio-economic, technological, climate changes, and forest loss was done to assess its impacts on water supply and demand in the area.\u003c/p\u003e \u003cp\u003eFurthermore, the hydrological model is semi-theoretical, continuous, semi-distributed, and deterministic. Given its semi-theoretical nature, the model requires calibration and verification. Notably, the WEAP system does not offer automatic calibration for the hydrological model, necessitating a manual implementation of the calibration process. The standard method calculates water demand by multiplying the activity level with the water use rate across various sectors, including industrial, municipal, and agricultural. This method applies to all sectors except for agriculture. To allocate resources among demands, WEAP utilizes a one-period linear programming routine, as outlined by Letcher et al. (2007) and Van Cauwenbergh et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Model calibration and validation\u003c/h2\u003e \u003cp\u003eThe parameters governing runoff generation from climate inputs underwent calibration and validation using historical streamflow observations from gauging stations in the Magat River Basin. Additionally, the calibration parameters of the WEAP model were manually adjusted, drawing on data collected, existing literature, and expert knowledge. Model performance evaluation utilized statistical indices, including the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data input and data collection\u003c/h2\u003e \u003cp\u003eTo simulate monthly hydrological processes in the WEAP hydrological model, meteorological/climate data are essential. This includes monthly information on precipitation, temperature, wind speed, and relative humidity, alongside Digital Elevation Model (DEM), land use, and streamflow data. The DEM and land use data were procured from the National Mapping and Resources Information Authority (NAMRIA). Meanwhile, climate and streamflow data were obtained from the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) and the National Irrigation Administration (NIA), respectively. Demand-related data, encompassing population water use rates, water consumption, agricultural demand, and population growth rates, were sourced from the National Water Resources Board (NWRB) and the Philippine Statistics Authority (PSA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Schematic description of Magat sub-basin WEAP model\u003c/h2\u003e \u003cp\u003eThe Magat River Basin WEAP model structure is divided into seven (7) hydrological catchments, namely Alimit, Subwatershed 2, Ibulao, Lamut, Subwatershed 1, Matuno, and Sta. Fe with corresponding percent areas of 14.82%, 10.83%, 15.13%, 7.64%, 14.61%, 14.70%, and 22.28% in the Magat Watershed, respectively. Specifically, there is one (1) municipality that may have been considered domestic, while one (1) is accounted for agricultural water use along the downstream of the Magat River Basin. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the schematic illustration of the model at Magat River Basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Future scenarios development\u003c/h2\u003e \u003cp\u003eWatershed-based water management represents a collaborative strategy that brings together all water stakeholders to formulate planned, concerted, and consensus-driven actions for the common good. This participatory approach involves individuals from diverse backgrounds, even when their interests may diverge. The WEAP system facilitates the creation of different current scenarios and their simulation in the future, addressing various \"what if\" questions, as discussed by Sieber \u0026amp; Purkey in 2015. These simulations can be compared with the existing situation. In addition to the reference scenario, four scenarios were developed for this study: the inclusion of additional Local Government Units (LGUs) as water users, changes in population growth rate, adoption of new irrigation techniques, and consideration of climate change. These scenarios aim to predict the future gap between water supply and demand. The identified management intervention options are designed to meet both current and future water demands at a minimal cost. The following section outlines the four scenarios developed:\u003c/p\u003e \u003cp\u003e \u003cem\u003eBaseline scenario (BS).\u003c/em\u003e The initial baseline scenario developed represents the present water supply and demand conditions within the watershed. This scenario forms the foundation for a more in-depth analysis of the current state and for making comparisons with other simulated scenarios. The data from the current accounts, covering the years 2000 to 2020, serves as the foundational information for the model. From this, various scenarios are created to investigate possible changes in the system in the years beyond the current accounts year, including 2025, 2030, 2050, and 2080, effectively extending the assessment of the current situation into the future. Furthermore, the domestic water demand for the baseline year was based on the actual population of the LGU who tapped Magat River as a source of their domestic water. The water users in the reference scenario are the current water users of the water resources in the basin which include the NIA-Magat River Integrated Irrigation System service areas and the LGU of Alfonso Lista. Also, the industry, mining, and other LGUs were added in the reference scenario as water users of the groundwater resources.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAdditional LGUs as water users.\u003c/em\u003e In this set of scenarios, the purpose of the Magat Dam for domestic water use was extended to LGU Santiago City outside the Magat River watershed. The baseline scenario for domestic water use is LGU-Alfonso Lista in Ifugao.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePopulation growth scenario.\u003c/em\u003e Under this scenario, the population growth was projected using the arithmetic method for 2025, 2030, 2050, and 2080. The population growth rate in Region 2 from 2000 to 2021 was 3.23 percent (PSA, 2020). There are four scenarios under this category, namely 2025, 2030, 2050, and 2080 population. The baseline scenario is the current domestic water usage of Alfonso Lista.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIrrigation system improvement scenario.\u003c/em\u003e The primary livelihood for the people in the Magat watershed is agriculture, but this fragile ecosystem is experiencing growing degradation due to a combination of several factors, including adverse climate changes that are already impacting local farmers. The impact of increased conveyance efficiency because of irrigation system improvement was evaluated at 95 percent. According to the Food and Agriculture Organization (FAO 1989), paving canals with concrete would achieve a conveyance efficiency of 95 percent. This technique allows for the improvement of conveyance efficiency due to the improvement measures. According to Alejo and Balderama (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the actual average conveyance efficiency of Magat is 76 percent. The water users from a certain LGU, specifically for irrigation and domestic purposes, are the same across scenarios. This is to allow assessment of the impact of improved conveyance efficiencies on water demand and unmet water demand. There were three scenarios used under conveyance efficiency, namely, 95 percent conveyance efficiency which is categorized as high, conveyance efficiency of 87 percent as moderate, and 80 percent conveyance efficiency as low. The alternate wetting and drying, with assumed water savings of 15 percent (low), 25 percent (moderate), and 30 percent (high) based on literature and the NIA Master Plan for 2020\u0026ndash;2030, was also simulated under the irrigation system improvement scenario.\u003c/p\u003e \u003cp\u003e \u003cem\u003eClimate change scenario.\u003c/em\u003e By using the Climate Information Risk Analysis Matrix (CLIRAM) tool, PAGASA was able to provide the downscaled projected changes in climate variables, particularly for rainfall and temperature in both mid (2036\u0026ndash;2065) and late 21st century (2070\u0026ndash;2099). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e as shown below quantifies the projections on the rainfall and mean temperature. These were inputted into the WEAP model after it was considered acceptable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProjected changes in seasonal Rainfall and mean temperature mid (2036\u0026ndash;2065) and late (2070\u0026ndash;2099) 21st century.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProjected Changes in Seasonal Rainfall in the Mid-21st Century (2036\u0026ndash;2065).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate Emission (RCP4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh Emission (RCP8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProjected Changes in Seasonal Mean Temperature in the Mid-21st Century (2036\u0026ndash;2065).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate Emission (RCP4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh Emission (RCP8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProjected Changes in Seasonal Rainfall in the Late-21st Century (2070\u0026ndash;2099).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate Emission (RCP4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh Emission (RCP8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProjected Changes in Seasonal Mean Temperature in the Late-21st Century (2070\u0026ndash;2099).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModerate Emission (RCP4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh Emission (RCP8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDJF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eForest loss scenario.\u003c/em\u003e The land use change scenario suggests a notable increase in agricultural land in the basin. This change can be attributed to the decrease in grassland which was converted into agricultural land. The forested land increased from 90,516.48 ha to 170,490.93 ha from 2006 to 2010. This may be attributed to the reforestation initiatives, people-participated reforestation, and afforestation led by NIA-MARIIS DRD. The urban areas also increased five-fold, possibly due to the increase of urbanization in the provinces where the watershed is located. Likewise, the water bodies in the basin are also seen expanding from 5,629.39 ha to 9,415.75 ha.\u003c/p\u003e \u003cp\u003eBased on the land-use change from 2010 to 2015, there is an increase in agricultural land by 21 percent due to the conversion of grasslands and forested lands into agricultural areas. On the other hand, the forested lands and water bodies of the basin are observed to be declining. Both areas have a 13 percent decrease from 2010 to 2015. There are three scenarios under this category, namely, 2030, 2050, and 2080 forest loss scenario.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model calibration and validation\u003c/h2\u003e \u003cp\u003eThe WEAP model calibration and validation over the Magat River Basin were performed using the streamflow data from the Magat River Gauge Station. The monthly streamflow recorded at the station was compared against the simulated values of the model from 2000\u0026ndash;2015 for the calibration period and 2016\u0026ndash;2020 for the validation period. The model performance was evaluated by calculating the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), the Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) for both calibration and validation periods. The efficiency of Nash-Sutcliffe varied from -\u0026infin; to 1 as stated by Gupta et al., in 2011. According to Ritter \u0026amp; Mu\u0026ntilde;oz-Carpena, in 2013, the effectiveness of 1 simply means that the modeled flow corresponds perfectly to the observed data.\u003c/p\u003e \u003cp\u003eIn addition, the simulated monthly streamflow matches the observed values satisfactory with NSE of 0.64 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.74 for the calibration period, and NSE\u0026thinsp;=\u0026thinsp;0.77 and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.83 for the validation period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In terms of PBIAS, it was estimated at -8.44 and 0.85 for the calibration and validation periods, respectively. This implies that the WEAP model accurately simulated the streamflow in the Magat watershed and can be a useful tool for modeling the impact of climate changes in the Philippine watersheds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe calibrated parameters used in the WEAP model for the Magat watershed is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Actually, there are nine influential parameters calibrated in the WEAP model using manual calibration. These parameters directly influence the streamflow along the Magat watershed. These parameters are preferred flow direction, Kc, root zone conductivity, runoff resistance factor, soil water capacity, deep water capacity, initial Z1, and initial Z2. The preferred flow direction, Kc, and Runoff resistance factor are sensitive to streamflow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibrated Parameters Used in the WEAP Model for the Magat Watershed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefault Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimized Values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreferred Flow Direction, (0 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKc, (0 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.80 to 10.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Zone Conductivity, (0.1 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 to 201.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRunoff Resistance Factor, (0 to 1000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99 to 358.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Water Capacity, (0 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531.36 to 594.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial Z1, (0 to 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep Conductivity, (0.1 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep Water Capacity, (0 and Higher)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial Z2, (0 to 100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline scenario\u003c/h2\u003e \u003cp\u003eBaseline scenario depicts the current situation of water demand and the availability in the watershed. It can be used to do a thorough analysis of the current situation to compare it with other simulated possibilities. The combined domestic and agriculture surface water has the highest water supply from the river, which is equal to 5431.11\u0026nbsp;million cubic meters (MCM), while the industry, mining, and domestic groundwater are 1485.85 MCM. Since the Magat Dam only covers surface water, it has a total water supply of 3,006.02 MCM for domestic and agricultural purposes.\u003c/p\u003e \u003cp\u003eThe domestic surface water has a water demand of 3.108 MCM and an unmet water demand of 0.747 MCM. On the other hand, agricultural surface water has a water demand of 3,217.547 MCM and an unmet water demand of 213.888 MCM. Additionally, the water demand for the industry, mining, and domestic groundwater has a corresponding value of 4.536 MCM, 114.510 MCM, and 115.011 MCM, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Most likely case scenario\u003c/h2\u003e \u003cp\u003eWater scarcity is exacerbated by population increase. Population increase reduces per capita water supply and drives people to nearby areas and cities already experiencing water shortages. Under this scenario, the population growth rate scenarios used are the baseline scenario and the increase in population growth rate. The model's base year is set as the current year (2020), and all the inputs were loaded into WEAP from which the results were generated.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the simulation results for the water demand (domestic surface water across population growth scenarios. For Scenario 1 (2025 population), results show a 9.46 percent increase in water demand relative to the baseline scenario. The results further indicate a slight increase in water demand for the 2025 population. Secondly, there is a percentage increase of 18.93 percent for Scenario 2 (2030 population) and 56.78 percent for Scenario 3 (2050 population). By 2080 (Scenario 4), the results show a percentage increase of 113.57 percent. Hence, the water demand for Scenario 4 is very high relative to the baseline scenario. The assumed growth rate per scenario was projected using an arithmetic method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePopulation growth is a significant cause of water scarcity. It contributes to the increase in demand for irrigation water. Given that the supply from the dam is enough to meet the projected water demand across population growth scenarios, improvement of irrigation systems is needed to address the unmet water demand. The impact of increased transport efficiency on water demand and unmet water demand was compared using two parameters: a) in terms of water Conveyance Efficiency (CE) where low (80%), moderate (87%), or high (95%) is a result of canal improvement which decreases the conveyance losses; and b) in terms of alternate wetting and drying (AWD) where low (15%), moderate (25%), and high (30%) of water is being saved decreasing the demand by 15, 25 and 30 percent which decreases the application losses.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Low CE and low AWD\u003c/h2\u003e \u003cp\u003eBased on Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, because of the combined low CE and low AWD interventions, the unmet water demand for domestic surface water by 2025 will decrease by 32.57 percent relative to the baseline. In 2030 and 2050, the unmet water demand will further decrease by 28.26 percent and 3.06 percent, respectively. On the other hand, in the year 2080, the unmet water demand will increase by 38.61 percent. In comparison, the impact of the combined low CE and low AWD interventions on unmet water demand for irrigation (agricultural surface water) will decrease at an average of 86.42 percent in all the timeframes of the simulation.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Impact of improving conveyance efficiency and alternative wetting and drying interventions on unmet water demand across population growth\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Moderate CE and low AWD\u003c/h2\u003e \u003cp\u003eSince the combined interventions of low CE and low AWD are not enough to supply the demand for water from 2025 to 2080 both for domestic and agricultural surface water, the combination of moderate CE and low AWD interventions was used to evaluate the unmet water demand for the projected population growth. As a result, the unmet water demand for agricultural surface water is zero in all the timeframes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), this means that the water savings from the combination of moderate CE and low AWD interventions are enough to meet the water demand in agricultural surface water (NIA-MARIIS service area) from 2025 to 2080. However, there is still unmet water demand on the domestic water surface. The unmet water demand for domestic surface water will decrease by 37.73 percent, 33.66 percent, and 14.68 percent for the scenarios 2025, 2030, and 2050, respectively, relative to the baseline scenario. In the 2080 scenario, the unmet water demand for domestic surface water will increase by 32.58 percent compared to the baseline. This means that the combination of moderate CE and low AWD interventions is still not enough to meet the water demand for domestic surface water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Moderate CE and moderate AWD\u003c/h2\u003e \u003cp\u003eSince the combined interventions of moderate CE and low AWD are still not enough to supply the demand for water from 2025 to 2080 for domestic surface water, the combination of moderate CE and moderate AWD interventions was used to evaluate the water and unmet water demand for population growth. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the unmet water demand for domestic surface water will significantly decrease by 65.67 percent, 55.72 percent, and 25.75 percent for the 2025, 2030, and 2050 scenarios, respectively. In the 2080 scenario, the unmet water demand will increase by 22.18 percent relative to the baseline. This means that the combination of moderate CE and moderate AWD interventions is still not enough to supply the demand for domestic surface water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 High CE and low AWD\u003c/h2\u003e \u003cp\u003eAs tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the impact of high CE and low AWD interventions on unmet water demand for domestic surface water across population growth since the combination of moderate CE and moderate AWD is still not enough to supply the needed water demand. The results show that there is a significant decrease of up to 74.41 percent for 2025, 74.36 percent for 2030, 63.72 percent for 2050, and 45.36 percent for the 2080 scenarios. With still a percentage of unmet water demand, this means that the combination of these interventions is still not enough to supply the water demand for domestic surface water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4. High CE and moderate AWD\u003c/h2\u003e \u003cp\u003eThe impact of high CE and moderate AWD will give a zero unmet water demand both for the domestic and agricultural surface water across all population growth scenarios. This means that 100% of the water demand for domestic and agricultural surface water is met at any time based on the simulations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe unmet water demand percentage increases and decreases for domestic and agricultural water users.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnmet Water Demand\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDomestic Surface Water, cu. m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003cp\u003e/Decrease (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgriculture Surface Water, cu. m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003cp\u003e/Decrease (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eReference Scenario\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e813,493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,966,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e879,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214,045,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,153,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214,336,096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,603,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214,758,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMost Likely Scenarios (Low CE and Low AWD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e503,958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(32.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,651,990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(86.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e536,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(28.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,736,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(86.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e724,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,116,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(86.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,035,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,664,728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(86.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModerate CE and Low AWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465,405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(37.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(33.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e637,674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(14.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e990,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModerate CE and Moderate AWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256,549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(65.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330,954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(55.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e554,940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(25.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e913,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHigh CE and Low AWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(74.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(74.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(63.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(45.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHigh CE and Moderate AWD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,887,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* No more unmet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Impact of the conveyance efficiency and alternate wetting and drying on unmet water demand with Santiago City as additional water users across population growth scenarios\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNow, what if Santiago City will tap water from the Magat River, obviously water demand will increase. To measure its impact, the combined high CE and moderate AWD interventions were used to evaluate the unmet water demand since this combined scenario was shown in earlier simulations as enough to supply water both for domestic and agricultural surface water. Simulation results show that water demand will increase by as high as 473.61 percent or 17.83\u0026nbsp;million cu m for the year 2025, 511.00 percent (18.99\u0026nbsp;million cu m) in 2030, 660.56 percent (23.64\u0026nbsp;million cu m) in 2050, and 884.91 percent (30.61\u0026nbsp;million cu m) in 2080 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite the huge increase in water demand for domestic in terms of population growth and with the addition of Santiago City as a water user, the water supply is still enough to supply water by 2025, 2030, 2050, and 2080. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the impact of the combination of high CE and moderate AWD interventions on unmet water demand for both domestic and agricultural surface water is now zero across all scenarios with Santiago City as an additional water user.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.6 Impacts of climate change on agricultural and domestic water\u003c/b\u003e user\u003c/h2\u003e \u003cp\u003eFuture climate change is projected to increase the variability of temperature and precipitation, which could significantly impact the agricultural sector. In this study, the average unmet water demand for agriculture and domestic purposes under the baseline scenario was estimated to be 388.28 MCM using the WEAP model. From the baseline scenario, the model predicts an increase in the future unmet water demand during the dry years of the mid and late 21st century for both Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios, while a decrease is projected during the normal and wet years.\u003c/p\u003e \u003cp\u003eDuring the dry years, unmet water demand will increase in the mid-21st century from 99.97 percent (429.21 MCM) to 117.34 percent (466.48 MCM) for RCP 4.5 and RCP 8.5, respectively. Likewise, in the late 21st century, there will be an estimated increase from 97.80 percent (424.55 MCM) to 108.68 percent (447.90 MCM). During this period, water demands are at the highest level while the water supply is low. Furthermore, rising temperatures are expected to hasten the evaporation of water and land surfaces and accelerate transpiration, thus, an increase in the unmet water demand is projected. Furthermore, during the normal and wet years, the unmet water demand is estimated to increase ranging from 61.68 to 83.46 percent and 15.68 to 27.38 percent, respectively.\u003c/p\u003e \u003cp\u003eThe water supply and demand for the climate change scenario are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The table reveals that the water supply accessible from the dam is at its peak during wet years across all the defined climate change scenarios. However, the water supply will not be sufficient to meet the future water demand. Correspondingly, unmet water demand is projected to be at its highest during the dry years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater supply and demand for climate change scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate Change Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Supply Accessible from the Dam, MCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Demand, MCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnmet Water Demand, MCM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMid-21st century (2036\u0026ndash;2065) - RCP 4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,791.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,826,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e393.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,947.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMid-21st century (2036\u0026ndash;2065) - RCP 8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,754.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,833.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,954.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLate 21st century (2070\u0026ndash;2099) - RCP 4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,796.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,847.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,972.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLate 21st century (2070\u0026ndash;2099) - RCP 8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,772.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,873.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,967.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,220.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Forest loss scenarios\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the simulation results of unmet water demand in the forest loss scenario. Specifically, it shows an increase of 99.3 percent with a value of 426.21 MCM in Scenario 1 (25% forest loss for 2030) relative to the baseline. Both Scenario 2 (36% forest loss for 2050) and Scenario 3 (53% forest loss for 2080) have a percentage increase of 99.7 percent and 100.1 percent, and corresponding values of 427.11 MCM and 427.99 MCM, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThe impact of climate change, land degradation, and increasing water consumption pertains to the projected increase in extreme weather events, complex rainfall patterns, land degradation due to deforestation, and escalating water demand. Addressing these challenges is imperative, emphasizing the need for advanced irrigation technologies. The study employed the WEAP model to assess the impact of various factors on water demand and unmet water demand in the Magat River Basin. Population growth is projected to increase water demand significantly in the coming decades, while low CE and low AWD interventions are shown to reduce unmet water demand, particularly in the short term, with a slight increase in 2080. High CE and moderate AWD interventions eliminate unmet water demand across all population growth scenarios. However, the addition of Santiago City as water user increases demand substantially, necessitating the implementation of high CE and moderate AWD interventions to meet this demand. Climate change is expected to exacerbate future unmet water demand, emphasizing the importance of intervention measures. Although the water supply is theoretically sufficient, accessibility issues and increasing demands pose challenges, highlighting the need for immediate implementation of high CE and moderate AWD interventions to improve water availability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests that could have appeared to influence the work in this research paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or any other support were received during the preparation of this research paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the relevant data will be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was made possible through the support of the National Economic Development Authority Region 02 for funding and with the cooperation of the DRD NIA MARIIS Division, Ramon, Isabela for providing the data used in the calibration of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026apos;s conception and design. Material preparation, data collection, and analysis were performed by E.R., L.A., O.B., JL.B., C.B., A.A., and C.A. The first draft of the manuscript was written by E.R., and all authors commented on previous versions of this study. All authors read and approved this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgarwal, S., Patil, J. P., Goyal, V. C., \u0026amp; Singh, A. (2018). Assessment of water supply\u0026ndash;demand using Water Evaluation and Planning (WEAP) model for Ur River Watershed, Madhya Pradesh, India. \u003cem\u003eJournal of The Institution of Engineers (India)\u003c/em\u003e, \u003cem\u003eSeries A, 100(1)\u003c/em\u003e, 21-32. https://doi.org/10.1007/s40030-018-0329-0.\u003c/li\u003e\n\u003cli\u003eAlejo, L. A., \u0026amp; Balderama, O. F. (2021). 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Modeling Climate Change Impact on the Inflow of the Magat Reservoir using the Soil and Water Assessment Tool (SWAT) Model for Dam Management. Journal of Water \u0026amp; Climate Change. 14(3): 633-650. https://doi.org/10.2166/wcc.2023.240.\u003c/li\u003e\n\u003cli\u003eTattao, E. (2010). \u003cem\u003eThe use of GIS and remote sensing in the assessment of Magat watershed in the Philippines\u003c/em\u003e [Master\u0026apos;s Thesis]. Massey University.\u003c/li\u003e\n\u003cli\u003eVan Cauwenbergh, N., Pinte, D., Tilmant, A., Frances, I., Pulido-Bosch, A., \u0026amp; Vanclooster, M. (2008). Multi-objective, multiple participant decision support for water management in the Andarax catchment, Almeria. \u003cem\u003eEnvironmental Geology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 479-489 https://ui.adsabs.harvard.edu/link_gateway/2008EnGeo..54..479V/doi:10.1007/s00254-007-0847-y.\u003c/li\u003e\n\u003cli\u003eYao, A. B., Mangoua, O., Georges, E. S., Kane, A., \u0026amp; Goula, B. (2021). Using \u0026ldquo;Water Evaluation and Planning\u0026rdquo; (WEAP) model to simulate water demand in Lobo watershed (Central-Western Cote d\u0026rsquo;Ivoire). \u003cem\u003eJournal of Water Resource and Protection\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 216-235. https://doi.org/10.4236/jwarp.2021.133013.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water resource management, Water supply and demand, Water Evaluation and Planning (WEAP), Water scarcity, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-4250146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4250146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study employs the Water Evaluation and Planning (WEAP) model to assess the impact of anticipated climate and socio-environmental changes on water scarcity in the Magat River Basin (MRB). The MRB is crucial for agriculture and domestic use but faces vulnerability due to climate change and societal factors. The current understanding of future water deficits in the MRB is limited, necessitating a comprehensive assessment. The research aims to evaluate the effects of socio-environmental changes on water supply and demand. Results show that strategic interventions like high conveyance efficiency and moderate Alternate Wetting and Drying techniques can mitigate unmet water demand caused by population growth and additional water users until 2080. However, climate change and forest loss are projected to exacerbate water scarcity, especially in agricultural regions dependent on water resources. Model simulations demonstrate the WEAP model's reliability in predicting streamflow. These findings underscore the need for targeted interventions and highlight the effects of climate change and forest loss on water resource management. The study recommends implementing high conveyance efficiency and moderate Alternate Wetting and Drying techniques to alleviate water scarcity and promote resilience, advancing integrated water resources planning and policy analysis.\u003c/p\u003e","manuscriptTitle":"Mitigating Future Water Scarcity Through Comprehensive Assessment of Climate and Socio-Environmental Impacts in River Basins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 06:31:36","doi":"10.21203/rs.3.rs-4250146/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-20T06:11:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"239730946785327748392002411126036035832","date":"2025-05-18T15:23:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311249997088469867924517818057789782557","date":"2024-12-18T05:28:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283716463782216765853407307102554195783","date":"2024-12-18T04:35:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282362535872923624640347388069745950709","date":"2024-09-01T04:59:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-30T09:08:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319467793920861864400684016864219285057","date":"2024-08-30T09:02:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195157708886332222880773594525382397667","date":"2024-08-29T05:08:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-26T08:08:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-12T06:22:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-12T06:22:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-04-11T04:06:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e0b7c8b2-c05b-4a01-a0ca-8cef8e0cb51d","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:07:16+00:00","versionOfRecord":{"articleIdentity":"rs-4250146","link":"https://doi.org/10.1007/s00704-025-05583-z","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-05-31 15:57:11","publishedOnDateReadable":"May 31st, 2025"},"versionCreatedAt":"2024-04-17 06:31:36","video":"","vorDoi":"10.1007/s00704-025-05583-z","vorDoiUrl":"https://doi.org/10.1007/s00704-025-05583-z","workflowStages":[]},"version":"v1","identity":"rs-4250146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4250146","identity":"rs-4250146","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}