Quantified Impact of Projected Climate Change on Groundwater Recharge and River Discharge Leveraging the Use of Open Access Geospatial Data

Quantified Impact of Projected Climate Change on Groundwater Recharge and River Discharge Leveraging the Use of Open Access Geospatial Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantified Impact of Projected Climate Change on Groundwater Recharge and River Discharge Leveraging the Use of Open Access Geospatial Data Julius Incillo Jimenez, Nitin Kumar Tripathi, Ashish Pandey, Sangam Shrestha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6333077/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In previous years, water is abundant in the Padsan River Watershed, even in tropical climates. However, with increasing variability due to global warming, the watershed faces disruption of water resources. This requires thorough study, yet with fragmented data management, it becomes challenging. With technological advantages, integrated water resources management (IWRM) becomes possible using open-access data to understand the potential impact of climate variability. The corrected Climate Hazards Group InfraRed Precipitation (CHIRPS) and ten Coupled Model Intercomparison Project Phase 6 (CMIP6) becomes the notable open-access data including request-based institutional data were used as inputs for the Soil and Water Assessment Tools (SWAT) to quantify the impact of climate change on river discharge and groundwater recharge. Results showed that all Shared Socioeconomic Pathways (SSPs) will disrupt river discharge and groundwater recharge, with a prominent increase in river discharge. Furthermore, SSP 585 in the 2090s has a more notable impact on river discharge than others. In contrast, the SSP 126 has a lesser impact but displays higher variability across the rest of the century. This important simulated observation highly supports the Philippine Development Plan (PDP) aims, which is that climate change's impact could disrupt existing infrastructure and recharge conservation while establishing recharge areas to combat water scarcity during the dry period in the watershed. In contrast to the PDP regarding groundwater use, the study also supports the increasing conjunctive use of surface and subsurface resources, given that comprehensive management of the subsurface extraction must be established based on the study’s results. Climate Change Geospatial Data Groundwater Recharge Quantile Mapping River Discharge CHIRPS CIMP6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION The Padsan River Watershed (PRW) is one of the key agricultural watersheds in the Philippines, offering essential resources that support crop production and benefit various community sectors. Like other watersheds, PRW serves as a vital water source, enabling settlement and sustaining livelihoods. Water as most crucial natural resources (Stigter et al., 2023 ) is essential for sustaining human life (Stigter et al., 2023 ; Yang et al., 2021 ; Anatolia S.M. Exposto et al., 2021; Kılıç Çetinkaya & Aslandoğan, 2022 ), supporting socioeconomic development (Alexandratos et al., 2019 ; Yang et al., 2021 ), and maintaining healthy ecosystems (Bogardi et al., 2020 ). For these notions, surface and subsurface freshwater resources are essential for global watershed availability and accessibility, supporting drinking, irrigation, industrial uses, and other terrestrial uses. However, these essential watershed resources are under threat from the impacts of climate change (Kılıç Çetinkaya & Aslandoğan, 2022 ), which is expected to alter the Earth’s hydrological cycle significantly (Yang et al., 2021 ) through changes in precipitation patterns, evapotranspiration rates, and temperature (Kılıç Çetinkaya & Aslandoğan, 2022 ). Concerns are growing about the potential consequences of climate change (Yang et al., 2021 ) on the recharge and dynamics of groundwater and surface water systems, as these processes are primary determinants of water availability. These alterations are already observed in PRW. The river discharge volume is projected to increase while the flow variability has enhanced (Tolentino et al., 2016 ), indicating more extreme hydrologic events. These extreme hydrologic events cause surplus water during the rainy season while deficit during the dry season and increased runoff from increasing rainfall intensity causes water inaccessibility (Alibuyog 2009 as cited by Northwestern University, 2024 ). Furthermore, Philippine irrigated agricultural expansions rely heavily on either the construction of surface irrigation systems or the exploitation of groundwater (Inocencio & Barker, 2018 ). These highlight the conjunctive use of surface and subsurface watersheds in agricultural production. Yet, altered recharge and discharge regimes can lead to the depletion of aquifers, reduced baseflow to rivers and streams, and changes in the frequency and magnitude of extreme hydrological events such as floods and droughts. These impacts can have profound implications for agricultural productivity, infrastructure investment inefficiency, rising water conflicts, economic crises, migration (Kılıç Çetinkaya & Aslandoğan, 2022 ), and overall resource management (Gleick, 2014 ; Kundzewicz et al., 2008 ; Taylor et al., 2012 ). Moreover, the PRW agricultural sector has suffered the most in recent years due to extreme climate events. Ilocos Norte, where the PRW provides shelter for half of the province, lost more than 644,000 USD in the agricultural sector due to the excessive drought (Lazaro, 2024 ). On the other hand, the region experiences six consecutive typhoons from October to mid-November 2024 (World Food Programme Philippines, 2024 ). One of these typhoons resulted in 283,528 metric tons (MT) of agricultural products being lost in the region, which includes the Province of Ilocos Norte. These notions ranked the nation’s 4th overall global climate risk index from 2000–2019 (Eckstein et al., 2021 ) to 1st in the 2024 world ranking according to the World Risk Index of 2023 (World Food Programme Philippines, 2024 ). Extreme events significantly impact agriculture, while climate-increasing variability exposes water resources’ vulnerability to degradation. Altered rainfall patterns (Rola et al., 2015 ; Tabios III, 2020 ; Velasco et al., 2020 ) could lead to water stress during the dry season, while excessive wet seasons exacerbate water scarcity and flood risks (Alejo, Ella, Lampayan, et al., 2021; Tolentino et al., 2016 ). The sea rise levels (Rola et al., 2015 ; Tabios III, 2020 ; Velasco et al., 2020 ) threaten coastal aquifers with saltwater intrusion, reducing freshwater availability (O’Neill et al., 2016 ; Tolentino et al., 2016 ) for the 60% of Filipinos living in coastal areas (D’Agnes et al., 2005 ). Furthermore, hydrologic studies conducted from various watersheds in the country show that river discharge will be altered, and reservoir management is in a challenging situation, posing a threat to water and food security and potentially increasing casualties during peak seasons, especially widely untested watersheds, e.g., PRW. Addressing these challenges that could impose the same impact on PRW necessitates a comprehensive understanding of the complex interplay between water resource availability and growing vulnerabilities driven by climate variability and extremes (Saedi et al., 2022 ; Yang et al., 2021 ). It is crucial for developing effective adaptation strategies and sustainable water management plans beneficial to Philippine watersheds. However, the formulation of sustainable solutions is constrained by the persistent scarcity of high-quality and reliable data, highlighting the urgent need for improved data collection, management, and analysis. These constraints could underpin comprehensive and effective management strategies (Rola et al., 2015 ; Valenzuela & Gutierrez, 2019 ; Velasco et al., 2020 ). As Tabios III ( 2020 ) emphasizes, the lack of sufficient monitoring infrastructure and the prevalence of sparse, discontinuous, and fragmented observatory data (Valenzuela & Gutierrez, 2019 ; Velasco et al., 2020 )impede the development of adequate water resources management frameworks. In this context, leveraging open-access datasets emerges as a vital step towards bridging these gaps and enabling informed decision-making for sustainable watershed management, ensuring highly efficient investment management in water resources infrastructure. These mentioned challenges apply to the case of the PRW as a mid-prioritized watershed of the nation that is widely untested in water resources. The watershed was heavily affected by recent climate variability and extremes. By quantifying the impacts of future climate change on groundwater recharge and river discharge within the watershed using free-access high-quality data and information, this study aims to inform local and regional water resources management efforts and contribute to the broader understanding of climate change’s influence on the hydrological cycle of the PRW that might be helpful to other nation’s watershed. To achieve this, the study will employ descriptive climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 provides the most up-to-date and comprehensive climate model projections, incorporating the latest scientific understanding of climate processes and their interactions. These projections will be instrumental in assessing future climate scenarios and their potential impacts on hydrological processes in the PRW (Eyring et al., 2016 ). Recent studies, such as Khoi et al. ( 2022 ), highlight the critical importance of integrating climate projections with hydrological models to predict changes in water resources, reinforcing the need for comprehensive climate impact assessments. Furthermore, the study uses the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS: Funk et al., 2015 ) as precipitation input. It was further bias-corrected using Quantile Mapping (Heo et al., 2019 ) to remove the systematic bias and ensure the monthly precipitation's variability aligned with the discrete ground-based observations. Other data model inputs were gathered from the institutional website, which provides free-access geospatial data maps dedicated to further scientific studies. The Soil and Water Assessment Tool (SWAT) model will also be utilized for hydrological modeling. SWAT is a robust, widely used model designed to predict the impact of land management practices on water, sediment, and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions over long periods (Arnold et al., 2012 ). Studies have demonstrated that SWAT can effectively simulate the impacts of climate change on hydrological processes, providing critical insights into future water availability and distribution (Khoi et al., 2022 ). Hydrological studies using SWAT have been shaping the understanding of water resources in the different watersheds in the country. It shows broad applicability across diverse watersheds (Alejo, Ella, & Saludes, 2021 ; Jamilla et al., 2021 ; Panondi & Izumi, 2021 ) for hydrological, climate change (Alejo & Alejandro, 2021 ; Singson et al., 2023 ) and land use and land cover (LULC) and urbanization (Boongaling et al., 2018 ; Jamilla et al., 2021 ) assessment. It also shows excellent allocations for hydropower potential assessment (Garcia et al., 2016 ; Tambong et al., 2019 ) and shaping policy and planning tools (Alejo & Alejandro, 2022 ; Guiamel & Lee, 2020 ). Various studies have also been conducted on Integrating CMIP6 climate projections with SWAT modeling, enabling a detailed analysis of how climate change could alter groundwater recharge and river discharge patterns. This comprehensive approach will provide critical insights into the magnitude and timing of these impacts, allowing water managers and policymakers to develop more informed and effective strategies for adapting to climate change and ensuring the long-term sustainability of the region’s water resources. This study aims to (1) integrate validated CHIRPS and other open-access data into the SWAT model, (2) simulate river discharge and groundwater recharge using the calibrated and validated model, (3) assess the projected impacts of climate change based on CMIP6 scenarios, and (4) recommend strategies for sustainable water resource management and projected climate change mitigation. MATERIAL AND METHODS Research Methods and Approach The study's conceptual framework (Fig. 1 ) illustrates the integrated research approach for achieving the desired outcomes. The framework provides a detailed systematic approach to quantify the impact of projected climate change on groundwater recharge (GWR) and river discharge (RiverQ), leveraging open-access data. It integrates open-access geospatial data with hydrological modeling to quantify the impact of projected climate change on groundwater recharge and river discharge. The integrated approach is structured into five multi-stage phases. The first phase is the data extraction and preprocessing for hydrological modeling, which includes model data input acquisition for the study area and rainfall bias correction. The second phase is the model simulation, calibration, and validation , which provides for Model Uncertainty Analysis . The third phase is the climate change scenario building , and the fourth is the impact simulation of climate change on river discharge data and groundwater recharge. The last one is visualizing the impact of climate change on selected water resources phenomena. Each phase ensures logical connectivity (Fig. 1 ) while leveraging robust datasets and modeling techniques. Each phase is further discussed in subsequent sections. Data Extraction and Preprocessing for Hydrological Modelling The study's first phase focuses on extracting and processing essential data inputs to support accurate hydrological simulations using the Soil and Water Assessment Tool (SWAT). Ensuring data quality and availability is critical for modeling the impacts of climate change (IPCC, 2023 ) on the Padsan River Watershed (PRW), a vulnerable agricultural watershed in the Philippines (Tolentino et al., 2016 ). Reliable datasets and robust pre-processing are essential to reduce model uncertainty and improve simulation accuracy (Tabios III, 2020 ). Moreover, PRW (Fig. 2 ) in northwestern Luzon covers 1,325 km 2 and extensively supports agriculture and urban development in Ilocos Norte Province (Provincial Government of Ilocos Norte, 2024 ). It has a climate type 1 based on the Modified Coronas Climate Classifications (Peralta et al., 2020 ). Climate variability and extreme weather events have increased hydrological risks, including altered river discharge patterns and potential groundwater depletion, threatening water security and agricultural productivity (Singson et al., 2023 ). Thus, a robust, data-driven integrated approach must be established. The study leverages open-access geospatial data to overcome data scarcity (Valenzuela & Gutierrez, 2019 ) in the watershed. The Digital Elevation Model (DEM), soil, Land Use Land Cover (LULC), and meteorological data were extracted during the data extraction, making them essential in hydrological modeling. The high spatial resolution of climate change scenarios was also extracted. The DEM was extracted from the Shuttle Radar Topography Mission (SRTM GL1) with a global resolution of 30 meters provided by the OpenTopography portal at https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1 (NASA Shuttle Radar Topography Mission 2013 ). The LULC and soil data of the National Mapping and Resource Information Authority (NAMRIA) and the Department of Agriculture (DA), respectively, were extracted from the Geoportal Philippines ( https://www.geoportal.gov.ph ). This is to ensure that recent information from the government was prioritized to ensure that results work alongside the current local classification of LULC and soil data followed by the Philippine government. Furthermore, discrete meteorological data was also extracted from the Philippines Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) at https://www.pagasa.dost.gov.ph/climate/climate-data following the licensing procedure and user agreement. The watershed has only one ground-based station, making the discrete station misinterpret most of the area. The acquired ground-based weather data validated the bias-corrected Satellite Precipitation Data extracted from the Climate Engine web portal ( https://www.climateengine.org ). Ground-based observation data for RiverQ were acquired from the National Irrigation Administration – Ilocos Norte (NIA-IN) with monthly available data from 2001 to 2021. These ground-based observational data were used to calibrate and validate the hydrological Model. The Satellite Precipitation Estimates (SPEs) used were the Climate Hazards Group InfraRed Precipitations with Station Data (CHIRPS). The CHIRPS dataset (Funk et al., 2015 ) is widely used as precipitation inputs due to its high spatial and temporal resolution, but it may contain systematic biases. To improve data accuracy, this study applies Nonparametric Quantile Mapping (QM), a bias correction method that aligns CHIRPS data with observed rainfall distributions (Heo et al., 2019 ). Studies show that QM improves hydrological simulations by reducing systematic biases while preserving climate trends (Cannon et al., 2015 ; Enayati et al., 2021 ). The QM method uses the empirical cumulative distribution function that Yersaw & Chane ( 2024 ) concluded performs well compared to other bias correction methods (BCM). Correcting CHIRPS data enhances its reliability for hydrological modeling, ensuring accurate projections of GWR and RiverQ in the PRW. Corrected CHIRPS augmented the shortage of available rainfall data in the watershed. The performance of the QM correction method was evaluated using selected statistical performance metrics. These parameters (Table 2 ) were assessed using four key criteria, including the model’s accuracy, bias and efficiency, data distribution, and uncertainty. The model’s accuracy was tested using the Nash Sutcliff Efficiency (NSE), Coefficient of Determination (R 2 ), Root Mean Error to Standard Deviation ratio (RSR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The system bias and efficiency were evaluated using the Percent Bias (PBIAS), Kling-Gupta Efficiency (KGE), and Standard Deviation Ratio (SDR). The data distribution was assessed using the quantiles and standard deviation ratio (SDR), while uncertainty was evaluated using the 95% confidence interval. Table 1 Statistical performance metrics for QM Bias Correction Method for the CHIRPS dataset (Jimenez et al., 2025 ) Statistical Parameters Uncorrected CHIRPS against Observed Precipitation Corrected CHIRPS against Observed Precipitation Model Accuracy Parameters Nash Sutcliff Efficiency (NSE, %) 0.87 0.85 Coefficient of Determination (R 2 ) 0.87 0.86 Root Mean Square Error (RMSE, mm) 96.09 100.29 Root Mean Error to Standard Deviation ratio (RSR) 0.36 0.38 Mean Absolute Error (MAE, mm) 55.27 52.39 System Bias and Efficiency Percent Bias (PBIAS, %) 1.76 -0.00 Kling-Gupta Efficiency (KGE) 0.86 0.93 Mean Bias Error (MBE) -0.51 ≈ 0.00 Data Distribution Metrics 25th Quantile, mm 6.26 1.20 50th Quantile, mm 72.90 54.65 75th Quantile, mm 310.40 277.8 Standard Deviation Ratio (SDR) 0.879 ≈ 1.00 Uncertainty Metrics 95% Confidence Interval (Lower: Upper, mm) 155.91: 199.21 178.06: 202.68 The corrected CHIRPS dataset has a slight reduction in accuracy. The NSE values slightly reduced from 0.87 to 0.85, while R 2 decreased from 0.87 to 0.86. Nevertheless, the corrected CHIRPS maintained its strong predictive performance. The RMSE has also indicated a slight increase from 4.20 mm while the MAE decreases by 2.88 mm. The slight rise in RMSE demonstrates that the correction has a slight impact on the overall error magnitude of the corrected compared to the uncorrected. At the same time, the increase in MAE produces a slight reduction in the average error magnitude. The RSR slightly increased by 2.88 mm, indicating a slight improvement in the overall error magnitude. The slight changes in both directions indicate negligible changes in the overall accuracy of the corrected CHIRPS to represent the monthly observed rainfall. On the other hand, the BIAS and system efficiency can significantly improve by the QM correction. PBIAS significantly improves to nearly zero, while the MBE of approximately zero demonstrated the significant reduction of the average difference between CHIRPS and observed precipitation from a negative bias, which indicates considerable underestimation of rainfall by the uncorrected CHIRPS. Furthermore, the overall evaluation parameter KGE has a more significant increase from 0.86 to 0.93, indicating that when bias, correlation, and variability are comprehensively evaluated at once, the correction makes a significant impact, making bias and variability dominate the slight decrease of accuracy parameters. The data distribution metrics also indicate an improved dataset for the CHIRPS. The SDR of corrected CHIRPS turns 1 from 0.869, suggesting that it efficiently reflects the variability of the ground-based rainfall. These justify that the variability and bias dominated the overall enhancement of the dataset over the slight change in accuracy. Furthermore, the 25th, 50 th, and 75th quantile significantly decreases from the uncorrected CHIRPS. These significant value reductions represented the monthly rainfall variability in the watershed, which had two pronounced seasons: wet and dry, where both give frequent extremes, vast rainfall during the rainy season, and scarcity of rainfall during the dry season. The results provide an overall enhancement of the corrected CHIRPS data. The reduction of uncertainty, aligning the variability of the corrected CHIRPS to the observed rainfall, and the reduction of systematic bias of the CHIRPS justify the suitability of the corrected CHIRPS as rainfall input for the Soil and Water Assessment Tool (SWAT). After the correction, all essential model inputs were processed into the desired format required by the SWAT model. Model Simulation, Uncertainties Analysis, Calibration and Validation The SWAT model has become a valuable tool in assessing the impacts of climate change on hydrological processes, providing flexibility to simulate complex watershed dynamics under changing climatic conditions. Widely used in climate change studies, SWAT integrates global and regional projections to quantify future water availability, including river discharge and groundwater recharge. For instance, SWAT was applied by the Mekong River Commission (MRC) to evaluate water resources management strategies in the Lower Mekong River Basin (LMRB) under different climate scenarios (MRC, 2015 ). Its long-term simulation capabilities make it suitable for assessing altered precipitation drivers of water resource dynamics in agricultural watersheds like PRW. SWAT facilitates integrated surface and subsurface water simulations (Arnold et al., 2012 ), providing a rapid and robust foundation for quantifying RiverQ and GWR for conjunctive use management. Its extensive user community has developed various coupled systems, enabling diverse hydrological applications. Given the study’s focus on utilizing open-access data, only the SWAT model was used, excluding more complex coupled models such as SWAT-MODFLOW. This approach aims to deliver reliable insights while emphasizing the need for greater transparency to achieve comprehensive water resource management outcomes. The developed model will be a foundation for broader applications in future studies, including integrating coupled systems. The ArcSWAT 2012.10_8.25 software, compatible with ArcMap 1.8.2, was used to simulate the SWAT model for the PRW over 20 years (2000–2020) using pre-processed spatial and climate data. SWAT-CUP with the SUFI-2 algorithm (Abbaspour et al., 2017 ; Khalid et al., 2016 ) was employed for uncertainty analysis, calibration, and validation, noted for its efficiency in handling large-scale models (Pandey et al., 2021 ). 16 sensitive SWAT parameters (Anaba et al., 2017 ) were considered during the uncertainty analysis (Abbaspour et al., 2017 ). The model was calibrated for river discharge from 2003 to 2014 and validated from 2015 to 2021 using the most sensitive parameters fitted values, ensuring reliability through uncertainty analysis and performance evaluation. Model performance was evaluated using statistical parameters, including NSE, PBIAS, R 2 , and RMSE, with sensitivity indicated by t-stat and p-values. In the uncertainty analysis, out of 16 identified sensitive SWAT parameters, four were revealed to be highly sensitive (Table 2 ). The baseflow factor ratio, SCS Runoff Curve Number for moisture condition II, Effective hydraulic conductivity, and moisture bulk density remain highly sensitive ( p < 0.00), affecting the comparative analysis between simulated and observed RiverQ. Singson et al. ( 2023 ) also identified these sensitive parameters as sensitive parameters and related studies. Their sensitivity is rooted directly in their direct influence on critical hydrological processes (Araza et al., 2021 ), such as surface runoff, infiltration, baseflow, and soil-water interactions, essential for accurately simulating RiverQ and GWR in PRW. Table 2 Sensitivity Analysis of SWAT Parameters with Corresponding through Global Sensitivity Analysis (Jimenez et al., 2025 ) SWAT Parameter Description Sensitivity Rank t-Stat P value Value Range (Anaba et al., 2017 ) Fitted (Optimal) Value 1. V_ALPHA_BNK.rte Baseflow alpha factor ratio 1 -14.94 0.00 0.00–1.00 0.03 2. R_CN2.mgt SCS Runoff Curve Number for moisture condition II 2 -14.10 0.00 0.00–2.00 0.08 3. R_SOL_K().sol Effective hydraulic conductivity (mm/hr) 3 -5.46 0.00 -0.25-0.50 -0.19 4. R_SOL_BD().sol Moist bulk density (g/cm 3 ) 4 -3.64 0.00 0.00–1.00 0.66 Afterward, the fitted values (Table 2 ) of the identified sensitive parameters yielded satisfactory calibration and validation results (Table 3 ). During the calibration period (2003–2014), the model achieved an NSE of 0.57, indicating that the model captured 57% of the variability in river discharge compared to observed values. An R² of 0.66 shows a moderate correlation between simulated and observed discharge, confirming the model’s ability to replicate trends in streamflow. The PBIAS of 2.80% indicates a minimal overestimation, which is acceptable within hydrological modeling standards (Moriasi et al., 2015 ). In the validation period (2015–2021), the model performance remained satisfactory with an NSE of 0.54, an R² of 0.71, and a PBIAS of 14.0%, although there was a slight decrease in NSE and an increase in PBIAS. Table 3 SWAT model calibration and validation evaluation statistics (Jimenez et al., 2025 ) STATISTICAL PARAMETERS CALIBRATION PERIOD (2003–2014) VALIDATION PERIOD (2015–2021) OBSERVED RiverQ SIMULATED RiverQ OBSERVED RiverQ SIMULATED RiverQ MEAN 3.54 3.44 3.56 3.06 ST DEV 2.73 3.07 2.41 2.89 NSE 0.57 0.54 R 2 0.66 0.71 r 0.81 0.84 PBIAS 2.80 14.0 RSR 0.66 0.68 The slight decline in NSE and rise in PBIAS can be attributed to extreme discharge misestimations commonly observed in SWAT simulation studies (Singson et al., 2023 ). NSE reductions indicate that the model’s ability to capture variability in extreme events has limitations, particularly during high-flow periods. PBIAS increases suggest that the model tends to overestimate streamflow, particularly in extreme months, which is consistent with findings from Du et al. ( 2024 ) and Alejo et al. ( 2021 ), who emphasized the need for local validation and multi-dataset comparisons to improve data accuracy in hydrological models. These results underscore the importance of interpreting the model’s outputs based on trends rather than absolute values to ensure reliable insights, particularly when assessing extreme hydrological events in data-scarce regions. With the increasing impact of climate variability, the Padsan River Watershed (PRW) has experienced significant changes in extreme events, necessitating further validation and refinement of hydrological models to account for these variabilities. More discussions on the works of Jimenez et al. ( 2025 ). Climate Change Model Selection and Scenario Building Climate change scenarios are essential for understanding future climatic conditions and their impacts on hydrological processes. These scenarios project changes in temperature and precipitation under various greenhouse gas emissions and socioeconomic pathways, providing projections for assessing potential impacts on water resources and ecosystems. The study utilized the Couple Model Intercomparison Project Phase 6 (CMIP6), which offers advanced climate simulations with improved model resolution and representation of physical processes compared to the previous version (CMIP5), offering reliable projections for hydrological studies (Eyring et al., 2016 ; O’Neill et al., 2016 ). To ensure consistency and robustness, the study adopts the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework, specifically the ISIMIP3b version, which integrates CMIP6 projections to downscaled and bias-corrected climate data (Lange & Büchner 2022 ). The ISIMIP framework ensures that the climate projections are standardized and comparable, supporting cross-sectoral assessments of climate impacts on water resources (Volkholz et al., 2022 ). The study extracted provincial-level downscaled monthly relative change data for precipitation and temperature from the ClimoCast platform ( https://a-plat.nies.go.jp/ap-plat/cmip6/global.html ) to represent the mean monthly changes in the PRW. The ClimoCast platform serves as the data provider for the ISIMIP3b (Lange & Büchner 2022 ). The ISIMIP3b data were sourced from 10 CMIP6 models, categorized into periods: Current Future (CF: 2025–2040), Near Future (NF: 2041–2060), Middle Future (MF: 2061–2080) and Far Future (FF: 2081–2100). The use of downscaled climate data from ISIMIP3b ensures high-resolution projections with bias correction, improving the accuracy of hydrological modeling for RiverQ and GWR in the PRW. This study considers climate projections from 10 models (Table 4 ) based on the latest CMIP6 data. These model are internationally recognized for their robust simulation of climate processes and have been validated through historical performance assessment. These models composed of 5 primary model that includes GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2HR, MRI-ESM2-0 and UKESM1-0-LL while the secondary model are CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth, and MIROC6 (Lange, 2021 ). These four climate scenarios (SSP126, SSP245, SSP3370, SSP585) (O’Neill et al., 2016 ) represent different future greenhouse gas (GHG) emissions pathways for the hydrologic impact simulations, providing the derive insight for broader applications and efficient information. Table 4 Selected 10 Climate Models utilized for the study Item Climate Model Extended Model Name Region Resources 1 CNRM-CM6-1 Centre National de Recherches Météorologiques Climate Model version 6.1 Europe Source: (Voldoire et al., 2019 ) 2 CNRM-ESM2-1 Centre National de Recherches Météorologiques Earth System Model version 2.1 Europe Source: (Séférian et al., 2019 ) 3 CanESM5 Canadian Earth System Model version 5 Canada Source: (Swart et al., 2019 ) 4 EC-Earth3 European Consortium Earth System Model version 3 Europe Source: (Döscher et al., 2022 ) 5 GFDL-ESM4 Geophysical Fluid Dynamics Laboratory Earth System Model version 4 USA Source: (Dunne et al., 2020 ) 6 IPSL-CM6A-LR Institut Pierre-Simon Laplace Climate Model version 6A Low Resolution France Source: (g et al., 2021) 7 MIROC6 Model for Interdisciplinary Research on Climate version 6 Japan, Asia Source: (Shiogama et al., 2023 ) 8 MPI-ESM1-2HR Max Planck Institute Earth System Model version 1.2 High Resolution Germany Source: (Müller et al., 2018 ) 9 MRI-ESM2-0 Meteorological Research Institute Earth System Model version 2.0 Japan, Asia Source: (Yukimoto et al., 2019 ) 10 UKESM1-0-LL Meteorological Research Institute Earth System Model version 2.0 United Kingdom Source: (Tang et al., 2019 ) * 1 – 5 is primary, 6 – 10 is secondary models By using state-of-the-art climate from CMIP6 and ISIMIP3b, the study ensures a comprehensive and consistent impact assessment, providing reliable insights for policy and decision-making regarding climate resilience in conjunctive water resources management. Climate Change Impact Simulation and Output Visualization The calibrated and validated model simulated the watershed hydrology from 2000 to 2021, including a three-year warming period (2000 to 2002) to establish a baseline for the simulation. This baseline supports the evaluation of climate change scenarios using the Shared Socioeconomic Pathways (SSPs) framework available from the ClimoCast data provider. SSP1 envisions a sustainable future with a shift toward inclusive development and environmental awareness, prioritizing education, health, and clean technology investments to reduce inequalities and enhance climate resilience. SSP2, labeled "Middle of the Road," describes a world where current trends continue, leading to moderate progress in sustainability with uneven regional development and persistent social inequalities. SSP3, known as "Regional Rivalry (A Rocky Road)," portrays a fragmented world focusing on regional interests, resulting in strong national identities, limited international cooperation, and weak environmental initiatives. SSP4, termed "Inequality (A Road Divided)," depicts a scenario of pronounced inequalities, where a technological elite thrives while the majority remains marginalized, leading to low social cohesion and significant environmental degradation. Finally, SSP5, "Fossil-Fueled Development (Taking the Highway)," illustrates a world characterized by rapid economic and technological growth driven by fossil fuels, resulting in high emissions but improved adaptive capacity through economic gains. These scenarios provide a comprehensive framework for quantifying the impacts of climate change on river discharge and groundwater recharge, as detailed by O’Neill et al. ( 2017 ). This study aimed to utilize these diverse pathways to understand the potential impact of future environmental changes and inform effective policy strategies for managing water resources under varying socioeconomic conditions. RESULTS AND DISCUSSION Integration of validated CHIRPS and other Open-access Model Data Inputs The fragmented management of data in the watershed presents challenges in delivering rapid and robust strategies for preserving water resources in the context of climate change and depleting resources. However, the increasing availability of open-access satellite data (such as CHIRPS) and high-resolution, bias-corrected CMIP6 climate projections offer opportunities to address those gaps. Similar to several studies highlighting the works of Alejo, Ella, & Saludes ( 2021 ), the use of CHIRP as rainfall input for SWAT watershed modeling in various climate types in the Philippines (Alejo & Alejandro, 2021 ), achieving satisfactory calibration and validation results (Table 3 ). On the other hand, CHIRPS performance varies across regions and requires local validation and bias correction to improve its reliability for hydrological modeling (Du et al., 2024 ). By integrating bias-corrected CHIRPS data and other open-access datasets into the SWAT model, this study achieved satisfactory performance in simulating river discharge and assumed to satisfactorily simulate the groundwater recharge, demonstrating the model’s potential to provide scientific insights for conjunctive surface and subsurface water management, particularly in data-scarce regions, like PRW. Moreover, the calibrated and validated SWAT model supports the study’s claims that it can accurately simulate river discharge (RiverQ) and groundwater recharge (GWR) in the Padsan River Watershed (PRW). The model effectively captures RiverQ variability, which aligns with the precipitation patterns observed in the CHIRPS dataset (Fig. 3 ). However, extreme hydrological events were overestimated, consistent with the findings of Du et al. ( 2024 ), who highlighted variability in CHIRPS performance across regions and its limitations in capturing extreme precipitation events while mentioning its strength in representing extremely low conditions. Similarly, Alejo et al. ( 2021 ) noted that CHIRPS may misrepresent localized rainfall patterns, particularly during high-intensity rainfall events, which can lead to biases in hydrological simulations. These discrepancies suggest that applying the study’s findings to other contexts requires additional validation and analysis, particularly for extreme hydrological conditions, to ensure accurate and reliable applications. Climate Change Projections in the PRW The monthly relative change of precipitation (Fig. 4 ) was extracted from the ISIMIP3b framework through the ClimoCast platform. Time series divided into four future timeframes. All scenarios indicate fluctuating rainfall patterns, with the 2030s showing minor variability while variability increases by the 2090s, which makes the projected impact of greenhouse gas emissions distinct. Furthermore, SSP126 shows moderate fluctuations in a low-emission scenario, indicating stable precipitation patterns. In contrast, SSP245 and SSP337 display intermediate to increasing variability along with higher timeframe. At the same time, SSP585, the high-emission scenario, exhibits extreme rainfall variations, with a pronounced peak by the 2090s, highlighting the potential for severe hydrological impacts. These findings correspond to the highlights provided by Tebaldi et al., ( 2021 ), which could lead to a heightened risk of extreme rainfall events that could lead to more frequent and severe flooding due to higher river flow while significantly altering groundwater recharge. The anticipated escalation in rainfall variability underscores the necessity for adaptive water resources management strategies that could mitigate the adverse effects on groundwater recharge cycles and river discharge patterns, enhancing the climate resilience of the watershed. This growing climate variability over time enables Philippine policies, e.g., the Integrated Water Resources Management Plan (IWRMP), to recommend actions with strict timelines for sustainable water use (Department of Environment and Natural Resources [DENR], 2024 ). Projected Impact of Climate Change on River Discharge The calibrated and validated SWAT model simulated future river discharge in the Padsan River Watershed under various climate change scenarios, comparing these projections with historical discharge data from 2000 to 2020. Figure 7 (Top Line Graph) shows that the historical river discharge displays seasonal fluctuations driven by annual precipitation cycles. From 2020 onward, all SSP scenarios predict an increase in river discharge, with higher-emission pathways such as SSP585 and SSP245 showing more substantial increases. The peaks in discharge are more pronounced under SSP585, reflecting more frequent and intense precipitation events, whereas SSP126 shows a moderate increase, closer to historical values, indicating that lower emissions result in less drastic changes. The relative changes in river discharge compared to the historical baseline (Table 5 , Fig. 7 ) show a consistent upward trend across all SSP scenarios. SSP585 exhibits the steepest increase, indicating more extreme shifts in water flow over time. Even under low-emission pathways like SSP126, the gradual rise in discharge highlights that future river flow will exceed historical norms, with relative changes ranging from 116–119%. Table 5 Mean Relative Changes of Net River Discharge of the Watershed CC Scenario Relative Change (%) Absolute Change (depth, mm) Mean Min Max Mean Min Max 2000–2020 * 723.77 ± 136.63 569.88 1,003.67 SSP126 119.55 ± 3.88 112.88 131.63 866.83 ± 143.10 674.44 1,174.19 SSP245 119.83 ± 4.36 110.97 132.75 868.59 ± 142.57 659.09 1,167.06 SSP370 116.44 ± 4.02 108.76 110.35 844.76 ± 142.89 643.80 1,164.35 SSP585 119.55 ± 4.91 110.35 134.25 866.72 ± 144.02 655.34 1,187.86 *Historical river discharge Furthermore, Table 4 provides the average net river discharge values (net and absolute) and each scenario's standard deviation for four future decades (2020 to 2100). High river discharge averaged around 723.77 m/s 3 , while the projected future discharge substantially increased across all SSP scenarios. The average discharge ranges from 844.75 m/s 3 under SSP370 to 868.83 m/s 3 under SSP126, reflecting the overall increase in water flow at the watershed outlet regardless of the emission pathways. Notably, the standard deviation remains relatively stable across all scenarios, indicating that while the absolute discharge values increase, the discharge variability (or fluctuation) is consistent with historical patterns. This suggests that while more water will flow through the watershed on average, the seasonal fluctuations and interannual variability will remain within a similar range, though at higher discharge levels. The relatively stable standard deviation values also imply that the watershed will continue to experience predictable seasonal cycles. Still, these will occur at a higher baseline discharge, particularly in higher-emission scenarios. The average relative changes in Table 4 support these claims. The average relative change remains consistent across all scenarios, with values ranging between 116% (SSP370) and 119% (SSP126 and SSP585). These relative changes indicate that river discharge will be approximately one-fourth higher than the historical baseline on average across the projection period. While the increase is relatively consistent, the standard deviation values remain predictable. SSP126 and SSP585 exhibit slightly higher averages than SSP245 and SSP370, suggesting that the lowest and highest emission scenarios will lead to more significant relative discharge increases than the medium-emission scenarios. The overall predictability of relative changes increases in river discharge over time, driven by climate change. These findings that capture the impact of CMIP6 on river discharge of PRW is comparable with the findings by Tolentino et al., ( 2016 ) that generally concluded a general increase of water flow for most Philippine watersheds. Similar to the findings of (Jimenez et al., 2022 ), the impact of climate change disrupts the seasonal pattern of discharge, affecting water availability. This comparison potentially aggravated current drought (Alonzo et al., 2023 ) and flooding (Graciela et al., 2018 ) events in the watershed and the province. On the other hand, the findings are comparable to the improvement of previous studies, which use the previous version of climate change. CMIP6 projects a broader range of future climate outcomes with higher radiative forcing scenarios and increased climate sensitivities leading to greater projections (Tebaldi et al., 2021 ). These boost its significant application in the watershed, that is highly vulnerable to flow variability due to its geographical location and challenging adaptation measures (Ignacio-Reardon & Luo, 2023 ). Projected Impact of Climate Change on Groundwater Recharge Considering the increasing importance of subsurface resources in the face of climate change, the fragmented institutional management (Valenzuela & Gutierrez, 2019 ) leads to scarce frameworks that challenge adaptation measures (Ignacio-Reardon & Luo, 2023 ) for the watershed. With this data scarcity in the watershed management, the reliance on the process-based model to provide the foundation in establishing governance of the subsurface resources recharge in the face of climate variability is essential, highly recommending the significant contribution of consolidated data management for institutions. The calibrated SWAT model simulated groundwater recharge from 2020 to 2100 under four SSP scenarios. The general trend (Fig. 6 ) shows periodic fluctuations in recharge driven by seasonal variability and a long-term increase across all scenarios. Higher emission scenarios (SSP585 and SSP245) exhibit steeper trends, indicating more significant increases in recharge rates, while SSP126 and SSP370 show moderate increases. This suggests that climate change, particularly under high-emission pathways, will lead to increased groundwater recharge, primarily due to intense precipitation events that may pose flooding risks rather than contributing to sustainable groundwater replenishment. In addition, the relative changes in mm/mm are also plotted in the projected futures. The relative changes highlight that SSP126 has the highest variability, with peaks reaching nearly 0.6, especially towards the latter half of the century. The climate low emission projection has the highest mean relative change (27.68%) over the rest of the 21st century (Table 6 ), which can fluctuate more compared with other climate change scenarios, as evidenced by the standard deviation to mean (± 11.42). It can increase from 12.21–64.34% from the current annual recharge, resulting in an estimated average annual groundwater change of 116.01 ± 218.79 mm. SSP245. Compared to SSP126, the other three climate change scenarios exhibit more stable relative changes, with SSP245 and SSP370 staying consistently lower. SSP245, the moderate emission, posts a relative change to the groundwater recharge of 27.61 ± 3.98%, which can increase the average annual groundwater recharge by 20.86–39.13%. This resulted in a 70.95 mm to 153.32 increase in the watershed groundwater recharge. SSP370, considered a high emission, has the lowest fluctuation of changes. The average relative change is 24.32 ± 3.98%, which resulted in an increase of 57.49 to 134.20 (mean of 103.28 mm ± 134.20) from the current annual groundwater recharge. While the high emission had the lowest average relative change, SSP585 was labeled as the extreme (or very high) emission, followed closely by SSP 245, having a relative change of 27.61 ± 3.96% with lesser fluctuations ranging from a minimum of 18–40.13%. In response, the projected increase in the current annual groundwater recharge was 77.59 mm to 157.23 mm. Table 6 Annual Watershed Relative and Absolute Groundwater Recharge of LRW. CC Scenario Relative Change (%) Absolute Change (depth, mm) Mean Min Max Mean Min Max SSP126 27.68 ± 11.42 12.21 64.34 116.01 ± 218.79 49.62 218.79 SSP245 27.61 ± 3.98 20.86 39.13 117.51 ± 153.32 70.95 153.32 SSP370 24.32 ± 3.98 16.91 34.25 103.28 ± 134.20 57.49 134.20 SSP585 27.26 ± 4.44 18.00 40.13 115.70 ± 157.23 77.59 157.23 The relative changes reflect significant deviations from the baseline recharge, driven by precipitation variability under climate forcing. The response of groundwater recharge rate on lower emissions exhibits extreme sensitivity to rainfall shifts compared to higher emissions with stable recharge rates within a narrower range. These findings highlight the groundwater recharge response to natural variability (Garcia-Menendez et al., 2017 ) compared to higher emissions with enhanced human-induced forcing. On the other hand, all SSPs indicate an increasing groundwater recharge rate, indicating the potential increase of the groundwater supply that can serve as a buffer of the increasing variability of climate, considering the sustainable extraction by incorporating the variability across time and space in the PRW, that could be implemented with thorough governing policies. Strengthening National Policies with Data-Driven Hydrological Changes The Philippines is taking a significant step toward sustainable water management with the proposed Department of Water Resources (National Water Resources Management Act S. No. 2771 19th Congress, 2024 ), addressing long-standing government fragmentation resulting in poor data management. The Philippines Development Plan (2023–2028; National Economic Development Authority [NEDA], 2023) highlights the need for science-based strategies, integrating hydrological models and satellite data to improve water planning and resilience. With climate variability complicating conjunctive water management, the PDP calls for Integrated Water Resource Management (IWRM) and ecosystem-based approaches (NEDA, 2023). Using the SWAT model reinforces the need for conjunctive water use planning by determining how erratic rainfall impacts river discharge and groundwater recharge. Frequent extreme weather events threaten water availability and infrastructure, making flood protection, water retention, and aquifer recharge strategies crucial. This study proposes an integrated conjunctive simulation framework, combining SWAT and satellite data, to enhance climate resilience. Aligning with policies, it supports climate-adjusted planning and conversation measures such as climate-smart farming, deforestation, and artificial recharge in PRW. The PDP also underscores the need for centralized water management to streamline policies and improve resilience (NEDA, 2023). This study supports investing in infrastructure rehabilitation and new projects to address shifting hydrological patterns. While the PDP warns against groundwater overexploit, this study finds that groundwater recharge increases under future climate scenarios. With proper governance, including artificial recharge initiatives, groundwater reservoirs can be a crucial buffer during dry periods. Science-driven water management is essential for informed decision-making. Hydrological simulations provide reliable data for monitoring watershed, ensuring sustainability policies for surface and groundwater use. The PDP’s emphasis on conjunctive management aligns with the study’s findings – enhancing groundwater recharge benefits communities, ecosystems, and long-term adaptation. Finally, this study highlights the need to protect and restore aquifer recharge zones, reinforcing PDP goals of safeguarding watersheds. Integrating data-driven hydrological modeling into national policies will strengthen water management and ensure sustainable water resources for future generations. CONCLUSION The SWAT model performed satisfactorily during the calibration (NSE = 0.57, R 2 = 0.66) and validation (NSE = 0.54, R 2 = 0.71). Slight misinterpretations of extreme weather events were recorded due to the climatic conditions of the watershed. Nevertheless, the satisfactory performance provides reliable insights into strengthening national policies and governance of the PRW and other places with similar watershed settings. The accessibility of five primary and five secondary CMIP6 climate projections gave an avenue to understand the projected impact of climate variability on River Discharge and Groundwater Recharge. Furthermore, these bias-corrected and downscaled climate models for projections allow the study to understand the temporal distribution for four futures in four SSPs. Results show that all SSPs will enhance rainfall variability, potentially disrupting river discharge and groundwater recharge current pattern threatening infrastructures and crop productions due to extreme climate conditions (floodings and droughts). The annual river discharge could increase by a minimum of 108.76% to a maximum increase of 134.25% across SSPs and timelines. Groundwater recharge could also increase by a minimum of 12.21% and a maximum of 40.13% across SSPs and timelines. These results supported PDP’s claims that integrated water resources management can capture excess water. The PDP also promotes the establishment of water infrastructure to combat droughts and reassess existing water infrastructure that could easily be disrupted due to the high variability of river discharge. Thus, the result of this study showcases the benefit of a data-driven integrated framework approach to strengthen the PRW policies and governance that could promote sustainability across watershed sectors. Declarations Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used Grammarly to improve the conciseness, flow, and writing tone of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take(s) full responsibility for the publication's content. Acknowledgment and Funding The authors would like to acknowledge the Department of Science and Technology – Science and Education Institute, Philippines, which provided research funds for the PhD Dissertation. Gratitude is also given to the Asian Institute of Technology, Thailand, and the Indian Institute of Technology – Roorkee, India, for providing laboratories to conduct the simulations. Other institutes worth noting are the Mariano Marcos State University and National Irrigation Administration – San Nicolas, Ilocos Norte, for providing ground-based data that was used to calibrate and validate the numerical model. Finally, most importantly, the Almighty provided strength and motivation to accomplish this work. References Abbaspour, K. C., Vaghefi, S., & Srinivasan, R. (2017). A guideline for successful calibration and uncertainty analysis for soil and water assessment: A review of papers from the 2016 international SWAT conference. In Water (Switzerland) (Vol. 10, Issue 1). MDPI AG. https://doi.org/10.3390/w10010006 Alejo, L. A., & Alejandro, A. S. (2021). Validating CHIRPS ability to estimate rainfall amount and detect rainfall occurrences in the Philippines. 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Two UCRD staff join climate change RND confab – Northwestern University . https://www.nwu.edu.ph/two-ucrd-staff-join-climate-change-rnd-confab?utm O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change , 42 , 169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004 O’Neill, B. C., Tebaldi, C., Van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J. F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development , 9 (9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016 Pandey, A., Bishal, K. C., Kalura, P., Chowdary, V. M., Jha, C. S., & Cerdà, A. (2021). A Soil Water Assessment Tool (SWAT) modeling approach to prioritize soil conservation management in River Basin Critical areas coupled with future climate scenario analysis. Air, Soil and Water Research , 14 . https://doi.org/10.1177/11786221211021395 Panondi, W., & Izumi, N. (2021). Climate Change Impact on the Hydrologic Regimes and Sediment Yield of Pulangi River Basin (PRB) for Watershed Sustainability. Sustainability 2021, Vol. 13, Page 9041 , 13 (16), 9041. https://doi.org/10.3390/SU13169041 Peralta, J. C. A. C., Narisma, G. T. T., & Cruz, F. A. T. (2020). Validation of high-resolution gridded rainfall datasets for climate applications in the Philippines. Journal of Hydrometeorology , 21 (7), 1571–1587. https://doi.org/10.1175/JHM-D-19-0276.1 Provincial Government of Ilocos Norte (25 July 2024). Food and Agriculture. Ilocos Norte and Investment Promotions Center. Retrieved from https://invest.ilocosnorte.gov.ph/opportunities/foodandagriculture on July 25, 2024 Rola, A. C., Pulhin, J. M., Tabios Iii, G. Q., Lizada, J. C., Helen, M., & Dayo, F. (2015). Challenges of Water Governance in the Philippines. In Philippine Journal of Science (Vol. 144, Issue 2). Saedi, J., Sharifi, M. R., Saremi, A., & Babazadeh, H. (2022). Assessing the impact of climate change and human activity on streamflow in a semiarid basin using precipitation and baseflow analysis. Scientific Reports 2022 12:1 , 12 (1), 1–17. https://doi.org/10.1038/s41598-022-13143-y Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J. F., Moine, M. P., Msadek, R., … Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate. Journal of Advances in Modeling Earth Systems , 11 (12), 4182–4227. https://doi.org/10.1029/2019MS001791 Shiogama, H., Tatebe, H., Hayashi, M., Abe, M., Arai, M., Koyama, H., Imada, Y., Kosaka, Y., Ogura, T., & Watanabe, M. (2023). MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses. Earth Syst. Dynam , 14 , 1107–1124. https://doi.org/10.5194/esd-14-1107-2023 Singson, C. L., Alejo, L. A., Balderama, O. F., Bareng, J. L. R., & Kantoush, S. A. (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 and Climate Change , 14 (3), 633–650. https://doi.org/10.2166/wcc.2023.240 Stigter, T. Y., Miller, J., Chen, J., & Re, V. (2023). Groundwater and climate change: threats and opportunities. Hydrogeology Journal , 31 (1), 7–10. https://doi.org/10.1007/S10040-022-02554-W/METRICS Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., … Winter, B. (2019). The Canadian Earth System Model version 5 (CanESM5.0.3). Geoscientific Model Development , 12 (11), 4823–4873. https://doi.org/10.5194/gmd-12-4823-2019 Tabios III, G. Q. (2020). World Water Resources Water Resources Systems of the Philippines: Modeling Studies. In V. P. Singh (Ed.), World Water Resources (Vol. 4). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-25401-8 Tambong, A., Bacordo, L., Martinez, K. Y., Molato, L. A., Semblante, O., & Garcia, P. (2019). Hydropower Potentials Estimation of Biliran Islands Based on Synthetic Aperture Radar Spatial Data Using Soil and Water Assessment Tool Simulation. Science and Humanities Journal , 13 (1), 83–98. https://doi.org/10.47773/SHJ.1998.121.7 Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., & Jones, C. (2019). MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical. Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Van Beek, R., Wada, Y., Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow, L., Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., Macdonald, A., Fan, Y., Maxwell, R. M., Yechieli, Y., … Treidel, H. (2012). Ground water and climate change. Nature Climate Change 2012 3:4 , 3 (4), 322–329. https://doi.org/10.1038/nclimate1744 Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O’Neill, B., Sanderson, B., Van Vuuren, D., Riahi, K., Meinshausen, M., Nicholls, Z., Tokarska, K., Hurtt, G., Kriegler, E., Meehl, G., Moss, R., … Ziehn, T. (2021). Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics , 12 (1), 253–293. https://doi.org/10.5194/esd-12-253-2021 Tolentino, P. L. M., Poortinga, A., Kanamaru, H., Keesstra, S., Maroulis, J., David, C. P. C., & Ritsema, C. J. (2016). Projected impact of climate change on hydrological regimes in the Philippines. PLoS ONE , 11 (10). https://doi.org/10.1371/journal.pone.0163941 Valenzuela, E. D., & Gutierrez, E. C. (2019). CPBRD Policy Brief Addressing Institutional Challenges in Water Resources Management . https://cpbrd.congress.gov.ph/images/PDF%20Attachments/CPBRD%20Policy%20Brief/PB2019-01_Addressing_Institutional_Challenges.pdf Velasco, L. G., Justine Diokno-Sicat, C., Faye Castillo, A. G., & Maddawin, R. B. (2020). The Philippine Local Government Water Sector . https://www.pids.gov.ph Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J. F., Michou, M., Moine, M. P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., … Waldman, R. (2019). Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1. Journal of Advances in Modeling Earth Systems , 11 (7), 2177–2213. https://doi.org/10.1029/2019MS001683 Volkholz, J., Lange, S., & Geiger, T. (2022). ISIMIP3a population input data . ISIMIP Repository. https://doi.org/https://doi.org/10.48364/ISIMIP.822480.2 World Food Programme Philippines. (2024). WFP Philippines 2024 Typhoon Season Situation Report #7 . https://www.wfp.org/countries/philippines Yang, D., Yang, Y., & Xia, J. (2021). Hydrological cycle and water resources in a changing world: A review. Geography and Sustainability , 2 (2), 115–122. https://doi.org/10.1016/J.GEOSUS.2021.05.003 Yersaw, B. T., & Chane, M. B. (2024). Regional climate models and bias correction methods for rainfall-runoff modeling in Katar watershed, Ethiopia. Environmental Systems Research 2024 13:1 , 13 (1), 1–22. https://doi.org/10.1186/S40068-024-00340-Z Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., & Ishii, M. (2019). The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931–965. https://doi.org/10.2151/JMSJ.2019-051 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6333077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442302505,"identity":"4c99141d-3fc9-4949-b62b-8622f67a786f","order_by":0,"name":"Julius Incillo Jimenez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACxgbmhgNAmoexvQFIGUhAxQ3waWGEauk5ANYiQVALSBOElkiAUAQdxjwjsfEwzx8GGeaZb8w+MBRY1Jk3MD8EMu7gtmNGYsNh3jagw2bnGM8AOUzmAJuxBIPBMwJaGkBacjeD/QJ0mRmQcRi/FqDDeBhnnoVpYf9GhBY2oJYZvDAtPARs6XnYcHAuyC89+Z8ZEgwkJGcw8xRLJODRYtiefPjDmz8M9obtx5IZPvyp45dgb9/44cMfPFoawNR/CCMBxGaGMXAAeQzGKBgFo2AUjAJ0AACx3kiPYOnVwQAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":true,"prefix":"","firstName":"Julius","middleName":"Incillo","lastName":"Jimenez","suffix":""},{"id":442302506,"identity":"543b4e8b-3566-4ada-845b-584c41acb513","order_by":1,"name":"Nitin Kumar Tripathi","email":"","orcid":"","institution":"Asian Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"Kumar","lastName":"Tripathi","suffix":""},{"id":442302507,"identity":"41f50ff7-3610-4308-bf8c-19c5907c667c","order_by":2,"name":"Ashish Pandey","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"","firstName":"Ashish","middleName":"","lastName":"Pandey","suffix":""},{"id":442302508,"identity":"2e138521-9aa1-4539-8274-995c5bc1cd2a","order_by":3,"name":"Sangam Shrestha","email":"","orcid":"","institution":"Asian Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sangam","middleName":"","lastName":"Shrestha","suffix":""},{"id":442302509,"identity":"bbb5c9c2-16de-4918-b684-2bcad186474a","order_by":4,"name":"Kuo Chieh Chao","email":"","orcid":"","institution":"Asian Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kuo","middleName":"Chieh","lastName":"Chao","suffix":""}],"badges":[],"createdAt":"2025-03-29 09:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6333077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6333077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81300268,"identity":"a186e86c-7ea6-4dea-9883-b136422ad5e3","added_by":"auto","created_at":"2025-04-24 13:51:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":459528,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework of the Study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/4c666fe0c5c581ae5e7699a8.png"},{"id":81300269,"identity":"7cfb65c1-9c30-488c-b06e-ce469df11edc","added_by":"auto","created_at":"2025-04-24 13:51:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":697097,"visible":true,"origin":"","legend":"\u003cp\u003eLocale of the Study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/9a2e70f242ff08d2b8e4769c.png"},{"id":81300266,"identity":"41211dc7-4f4e-45fe-a57f-b3609e7d28a9","added_by":"auto","created_at":"2025-04-24 13:51:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219770,"visible":true,"origin":"","legend":"\u003cp\u003eTime series Plot of simulated and observed river discharge during the calibration and validation period.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/56f27e82512aa5e25f5110e6.png"},{"id":81301109,"identity":"28a11166-8b22-4b1e-bc24-d18277ee7899","added_by":"auto","created_at":"2025-04-24 13:59:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165394,"visible":true,"origin":"","legend":"\u003cp\u003eThe Average Monthly Relative Changes (difference) of the ten climate models.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/81cd2231dbb74c084646341e.png"},{"id":81300275,"identity":"0d5f5436-769b-4747-bf25-855d3cdd5940","added_by":"auto","created_at":"2025-04-24 13:51:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":454242,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7. Simulated and Projected Watershed Net River Discharge\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/05e769a8c560b36cd73454a4.png"},{"id":81301841,"identity":"cdc3212c-cf3b-4d6d-86ca-43adcc2eb206","added_by":"auto","created_at":"2025-04-24 14:07:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":330642,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6. Simulated and Projected Groundwater Recharge (Absolute and Relative)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/414c347e395d28de754750bc.png"},{"id":106723627,"identity":"99d22e82-99b6-4e8d-ba33-d380ced53560","added_by":"auto","created_at":"2026-04-12 18:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3591046,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6333077/v1/1922b762-9a47-4ab7-be74-d50841ea29ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantified Impact of Projected Climate Change on Groundwater Recharge and River Discharge Leveraging the Use of Open Access Geospatial Data","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe Padsan River Watershed (PRW) is one of the key agricultural watersheds in the Philippines, offering essential resources that support crop production and benefit various community sectors. Like other watersheds, PRW serves as a vital water source, enabling settlement and sustaining livelihoods. Water as most crucial natural resources (Stigter et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) is essential for sustaining human life (Stigter et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Anatolia S.M. Exposto et al., 2021; Kılı\u0026ccedil; \u0026Ccedil;etinkaya \u0026amp; Aslandoğan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), supporting socioeconomic development (Alexandratos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and maintaining healthy ecosystems (Bogardi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For these notions, surface and subsurface freshwater resources are essential for global watershed availability and accessibility, supporting drinking, irrigation, industrial uses, and other terrestrial uses.\u003c/p\u003e \u003cp\u003eHowever, these essential watershed resources are under threat from the impacts of climate change (Kılı\u0026ccedil; \u0026Ccedil;etinkaya \u0026amp; Aslandoğan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which is expected to alter the Earth\u0026rsquo;s hydrological cycle significantly (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) through changes in precipitation patterns, evapotranspiration rates, and temperature (Kılı\u0026ccedil; \u0026Ccedil;etinkaya \u0026amp; Aslandoğan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Concerns are growing about the potential consequences of climate change (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on the recharge and dynamics of groundwater and surface water systems, as these processes are primary determinants of water availability. These alterations are already observed in PRW. The river discharge volume is projected to increase while the flow variability has enhanced (Tolentino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), indicating more extreme hydrologic events. These extreme hydrologic events cause surplus water during the rainy season while deficit during the dry season and increased runoff from increasing rainfall intensity causes water inaccessibility (Alibuyog 2009 as cited by Northwestern University, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, Philippine irrigated agricultural expansions rely heavily on either the construction of surface irrigation systems or the exploitation of groundwater (Inocencio \u0026amp; Barker, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These highlight the conjunctive use of surface and subsurface watersheds in agricultural production. Yet, altered recharge and discharge regimes can lead to the depletion of aquifers, reduced baseflow to rivers and streams, and changes in the frequency and magnitude of extreme hydrological events such as floods and droughts. These impacts can have profound implications for agricultural productivity, infrastructure investment inefficiency, rising water conflicts, economic crises, migration (Kılı\u0026ccedil; \u0026Ccedil;etinkaya \u0026amp; Aslandoğan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and overall resource management (Gleick, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kundzewicz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the PRW agricultural sector has suffered the most in recent years due to extreme climate events. Ilocos Norte, where the PRW provides shelter for half of the province, lost more than 644,000 USD in the agricultural sector due to the excessive drought (Lazaro, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, the region experiences six consecutive typhoons from October to mid-November 2024 (World Food Programme Philippines, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One of these typhoons resulted in 283,528 metric tons (MT) of agricultural products being lost in the region, which includes the Province of Ilocos Norte. These notions ranked the nation\u0026rsquo;s 4th overall global climate risk index from 2000\u0026ndash;2019 (Eckstein et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to 1st in the 2024 world ranking according to the World Risk Index of 2023 (World Food Programme Philippines, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExtreme events significantly impact agriculture, while climate-increasing variability exposes water resources\u0026rsquo; vulnerability to degradation. Altered rainfall patterns (Rola et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tabios III, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Velasco et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) could lead to water stress during the dry season, while excessive wet seasons exacerbate water scarcity and flood risks (Alejo, Ella, Lampayan, et al., 2021; Tolentino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The sea rise levels (Rola et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tabios III, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Velasco et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) threaten coastal aquifers with saltwater intrusion, reducing freshwater availability (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tolentino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for the 60% of Filipinos living in coastal areas (D\u0026rsquo;Agnes et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, hydrologic studies conducted from various watersheds in the country show that river discharge will be altered, and reservoir management is in a challenging situation, posing a threat to water and food security and potentially increasing casualties during peak seasons, especially widely untested watersheds, e.g., PRW. Addressing these challenges that could impose the same impact on PRW necessitates a comprehensive understanding of the complex interplay between water resource availability and growing vulnerabilities driven by climate variability and extremes (Saedi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is crucial for developing effective adaptation strategies and sustainable water management plans beneficial to Philippine watersheds.\u003c/p\u003e \u003cp\u003eHowever, the formulation of sustainable solutions is constrained by the persistent scarcity of high-quality and reliable data, highlighting the urgent need for improved data collection, management, and analysis. These constraints could underpin comprehensive and effective management strategies (Rola et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Valenzuela \u0026amp; Gutierrez, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Velasco et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As Tabios III (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasizes, the lack of sufficient monitoring infrastructure and the prevalence of sparse, discontinuous, and fragmented observatory data (Valenzuela \u0026amp; Gutierrez, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Velasco et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)impede the development of adequate water resources management frameworks. In this context, leveraging open-access datasets emerges as a vital step towards bridging these gaps and enabling informed decision-making for sustainable watershed management, ensuring highly efficient investment management in water resources infrastructure.\u003c/p\u003e \u003cp\u003eThese mentioned challenges apply to the case of the PRW as a mid-prioritized watershed of the nation that is widely untested in water resources. The watershed was heavily affected by recent climate variability and extremes. By quantifying the impacts of future climate change on groundwater recharge and river discharge within the watershed using free-access high-quality data and information, this study aims to inform local and regional water resources management efforts and contribute to the broader understanding of climate change\u0026rsquo;s influence on the hydrological cycle of the PRW that might be helpful to other nation\u0026rsquo;s watershed. To achieve this, the study will employ descriptive climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 provides the most up-to-date and comprehensive climate model projections, incorporating the latest scientific understanding of climate processes and their interactions. These projections will be instrumental in assessing future climate scenarios and their potential impacts on hydrological processes in the PRW (Eyring et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recent studies, such as Khoi et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), highlight the critical importance of integrating climate projections with hydrological models to predict changes in water resources, reinforcing the need for comprehensive climate impact assessments.\u003c/p\u003e \u003cp\u003eFurthermore, the study uses the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS: Funk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as precipitation input. It was further bias-corrected using Quantile Mapping (Heo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to remove the systematic bias and ensure the monthly precipitation's variability aligned with the discrete ground-based observations. Other data model inputs were gathered from the institutional website, which provides free-access geospatial data maps dedicated to further scientific studies.\u003c/p\u003e \u003cp\u003eThe Soil and Water Assessment Tool (SWAT) model will also be utilized for hydrological modeling. SWAT is a robust, widely used model designed to predict the impact of land management practices on water, sediment, and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions over long periods (Arnold et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies have demonstrated that SWAT can effectively simulate the impacts of climate change on hydrological processes, providing critical insights into future water availability and distribution (Khoi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hydrological studies using SWAT have been shaping the understanding of water resources in the different watersheds in the country. It shows broad applicability across diverse watersheds (Alejo, Ella, \u0026amp; Saludes, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jamilla et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Panondi \u0026amp; Izumi, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for hydrological, climate change (Alejo \u0026amp; Alejandro, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and land use and land cover (LULC) and urbanization (Boongaling et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jamilla et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) assessment. It also shows excellent allocations for hydropower potential assessment (Garcia et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tambong et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and shaping policy and planning tools (Alejo \u0026amp; Alejandro, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guiamel \u0026amp; Lee, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Various studies have also been conducted on Integrating CMIP6 climate projections with SWAT modeling, enabling a detailed analysis of how climate change could alter groundwater recharge and river discharge patterns. This comprehensive approach will provide critical insights into the magnitude and timing of these impacts, allowing water managers and policymakers to develop more informed and effective strategies for adapting to climate change and ensuring the long-term sustainability of the region\u0026rsquo;s water resources.\u003c/p\u003e \u003cp\u003eThis study aims to (1) integrate validated CHIRPS and other open-access data into the SWAT model, (2) simulate river discharge and groundwater recharge using the calibrated and validated model, (3) assess the projected impacts of climate change based on CMIP6 scenarios, and (4) recommend strategies for sustainable water resource management and projected climate change mitigation.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Methods and Approach\u003c/h2\u003e \u003cp\u003eThe study's conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the integrated research approach for achieving the desired outcomes. The framework provides a detailed systematic approach to quantify the impact of projected climate change on groundwater recharge (GWR) and river discharge (RiverQ), leveraging open-access data. It integrates open-access geospatial data with hydrological modeling to quantify the impact of projected climate change on groundwater recharge and river discharge. The integrated approach is structured into five multi-stage phases. The first phase is the \u003cem\u003edata extraction and preprocessing\u003c/em\u003e for hydrological modeling, which includes model data input acquisition for the study area and rainfall bias correction. The second phase is the \u003cem\u003emodel simulation, calibration, and validation\u003c/em\u003e, which provides for \u003cem\u003eModel Uncertainty Analysis\u003c/em\u003e. The third phase is the \u003cem\u003eclimate change scenario building\u003c/em\u003e, and the fourth is the \u003cem\u003eimpact simulation of climate change\u003c/em\u003e on river discharge data and groundwater recharge. The last one is \u003cem\u003evisualizing the impact of climate change\u003c/em\u003e on selected water resources phenomena. Each phase ensures logical connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) while leveraging robust datasets and modeling techniques. Each phase is further discussed in subsequent sections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Extraction and Preprocessing for Hydrological Modelling\u003c/h3\u003e\n\u003cp\u003eThe study's first phase focuses on extracting and processing essential data inputs to support accurate hydrological simulations using the Soil and Water Assessment Tool (SWAT). Ensuring data quality and availability is critical for modeling the impacts of climate change (IPCC, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on the Padsan River Watershed (PRW), a vulnerable agricultural watershed in the Philippines (Tolentino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Reliable datasets and robust pre-processing are essential to reduce model uncertainty and improve simulation accuracy (Tabios III, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, PRW (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in northwestern Luzon covers 1,325 km\u003csup\u003e2\u003c/sup\u003e and extensively supports agriculture and urban development in Ilocos Norte Province (Provincial Government of Ilocos Norte, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It has a climate type 1 based on the Modified Coronas Climate Classifications (Peralta et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate variability and extreme weather events have increased hydrological risks, including altered river discharge patterns and potential groundwater depletion, threatening water security and agricultural productivity (Singson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, a robust, data-driven integrated approach must be established.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study leverages open-access geospatial data to overcome data scarcity (Valenzuela \u0026amp; Gutierrez, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in the watershed. The Digital Elevation Model (DEM), soil, Land Use Land Cover (LULC), and meteorological data were extracted during the data extraction, making them essential in hydrological modeling. The high spatial resolution of climate change scenarios was also extracted. The DEM was extracted from the Shuttle Radar Topography Mission (SRTM GL1) with a global resolution of 30 meters provided by the OpenTopography portal at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1\u003c/span\u003e\u003cspan address=\"https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (NASA Shuttle Radar Topography Mission \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The LULC and soil data of the National Mapping and Resource Information Authority (NAMRIA) and the Department of Agriculture (DA), respectively, were extracted from the Geoportal Philippines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geoportal.gov.ph\u003c/span\u003e\u003cspan address=\"https://www.geoportal.gov.ph\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This is to ensure that recent information from the government was prioritized to ensure that results work alongside the current local classification of LULC and soil data followed by the Philippine government. Furthermore, discrete meteorological data was also extracted from the Philippines Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pagasa.dost.gov.ph/climate/climate-data\u003c/span\u003e\u003cspan address=\"https://www.pagasa.dost.gov.ph/climate/climate-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e following the licensing procedure and user agreement. The watershed has only one ground-based station, making the discrete station misinterpret most of the area. The acquired ground-based weather data validated the bias-corrected Satellite Precipitation Data extracted from the Climate Engine web portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.climateengine.org\u003c/span\u003e\u003cspan address=\"https://www.climateengine.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Ground-based observation data for RiverQ were acquired from the National Irrigation Administration \u0026ndash; Ilocos Norte (NIA-IN) with monthly available data from 2001 to 2021. These ground-based observational data were used to calibrate and validate the hydrological Model. The Satellite Precipitation Estimates (SPEs) used were the Climate Hazards Group InfraRed Precipitations with Station Data (CHIRPS).\u003c/p\u003e \u003cp\u003eThe CHIRPS dataset (Funk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is widely used as precipitation inputs due to its high spatial and temporal resolution, but it may contain systematic biases. To improve data accuracy, this study applies Nonparametric Quantile Mapping (QM), a bias correction method that aligns CHIRPS data with observed rainfall distributions (Heo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies show that QM improves hydrological simulations by reducing systematic biases while preserving climate trends (Cannon et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Enayati et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The QM method uses the empirical cumulative distribution function that Yersaw \u0026amp; Chane (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) concluded performs well compared to other bias correction methods (BCM). Correcting CHIRPS data enhances its reliability for hydrological modeling, ensuring accurate projections of GWR and RiverQ in the PRW. Corrected CHIRPS augmented the shortage of available rainfall data in the watershed.\u003c/p\u003e \u003cp\u003eThe performance of the QM correction method was evaluated using selected statistical performance metrics. These parameters (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were assessed using four key criteria, including the model\u0026rsquo;s accuracy, bias and efficiency, data distribution, and uncertainty. The model\u0026rsquo;s accuracy was tested using the Nash Sutcliff Efficiency (NSE), Coefficient of Determination (R\u003csup\u003e2\u003c/sup\u003e), Root Mean Error to Standard Deviation ratio (RSR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The system bias and efficiency were evaluated using the Percent Bias (PBIAS), Kling-Gupta Efficiency (KGE), and Standard Deviation Ratio (SDR). The data distribution was assessed using the quantiles and standard deviation ratio (SDR), while uncertainty was evaluated using the 95% confidence interval.\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\u003eStatistical performance metrics for QM Bias Correction Method for the CHIRPS dataset (Jimenez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\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\u003eStatistical Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUncorrected CHIRPS against Observed Precipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrected CHIRPS against Observed Precipitation\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\u003e\u003cem\u003eModel Accuracy Parameters\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNash Sutcliff Efficiency (NSE, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient of Determination (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Mean Square Error (RMSE, mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e96.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRoot Mean Error to Standard Deviation ratio (RSR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Absolute Error (MAE, mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e55.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSystem Bias and Efficiency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent Bias (PBIAS, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKling-Gupta Efficiency (KGE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Bias Error (MBE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eData Distribution Metrics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th Quantile, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50th Quantile, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e72.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75th Quantile, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e310.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e277.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation Ratio (SDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026asymp;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUncertainty Metrics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% Confidence Interval (Lower: Upper, mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e155.91: 199.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178.06: 202.68\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\u003eThe corrected CHIRPS dataset has a slight reduction in accuracy. The NSE values slightly reduced from 0.87 to 0.85, while R\u003csup\u003e2\u003c/sup\u003e decreased from 0.87 to 0.86. Nevertheless, the corrected CHIRPS maintained its strong predictive performance. The RMSE has also indicated a slight increase from 4.20 mm while the MAE decreases by 2.88 mm. The slight rise in RMSE demonstrates that the correction has a slight impact on the overall error magnitude of the corrected compared to the uncorrected. At the same time, the increase in MAE produces a slight reduction in the average error magnitude. The RSR slightly increased by 2.88 mm, indicating a slight improvement in the overall error magnitude. The slight changes in both directions indicate negligible changes in the overall accuracy of the corrected CHIRPS to represent the monthly observed rainfall.\u003c/p\u003e \u003cp\u003eOn the other hand, the BIAS and system efficiency can significantly improve by the QM correction. PBIAS significantly improves to nearly zero, while the MBE of approximately zero demonstrated the significant reduction of the average difference between CHIRPS and observed precipitation from a negative bias, which indicates considerable underestimation of rainfall by the uncorrected CHIRPS. Furthermore, the overall evaluation parameter KGE has a more significant increase from 0.86 to 0.93, indicating that when bias, correlation, and variability are comprehensively evaluated at once, the correction makes a significant impact, making bias and variability dominate the slight decrease of accuracy parameters.\u003c/p\u003e \u003cp\u003eThe data distribution metrics also indicate an improved dataset for the CHIRPS. The SDR of corrected CHIRPS turns 1 from 0.869, suggesting that it efficiently reflects the variability of the ground-based rainfall. These justify that the variability and bias dominated the overall enhancement of the dataset over the slight change in accuracy. Furthermore, the 25th, 50\u003csup\u003eth,\u003c/sup\u003e and 75th quantile significantly decreases from the uncorrected CHIRPS. These significant value reductions represented the monthly rainfall variability in the watershed, which had two pronounced seasons: wet and dry, where both give frequent extremes, vast rainfall during the rainy season, and scarcity of rainfall during the dry season.\u003c/p\u003e \u003cp\u003eThe results provide an overall enhancement of the corrected CHIRPS data. The reduction of uncertainty, aligning the variability of the corrected CHIRPS to the observed rainfall, and the reduction of systematic bias of the CHIRPS justify the suitability of the corrected CHIRPS as rainfall input for the Soil and Water Assessment Tool (SWAT). After the correction, all essential model inputs were processed into the desired format required by the SWAT model.\u003c/p\u003e\n\u003ch3\u003eModel Simulation, Uncertainties Analysis, Calibration and Validation\u003c/h3\u003e\n\u003cp\u003eThe SWAT model has become a valuable tool in assessing the impacts of climate change on hydrological processes, providing flexibility to simulate complex watershed dynamics under changing climatic conditions. Widely used in climate change studies, SWAT integrates global and regional projections to quantify future water availability, including river discharge and groundwater recharge. For instance, SWAT was applied by the Mekong River Commission (MRC) to evaluate water resources management strategies in the Lower Mekong River Basin (LMRB) under different climate scenarios (MRC, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Its long-term simulation capabilities make it suitable for assessing altered precipitation drivers of water resource dynamics in agricultural watersheds like PRW. SWAT facilitates integrated surface and subsurface water simulations (Arnold et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), providing a rapid and robust foundation for quantifying RiverQ and GWR for conjunctive use management. Its extensive user community has developed various coupled systems, enabling diverse hydrological applications. Given the study\u0026rsquo;s focus on utilizing open-access data, only the SWAT model was used, excluding more complex coupled models such as SWAT-MODFLOW. This approach aims to deliver reliable insights while emphasizing the need for greater transparency to achieve comprehensive water resource management outcomes. The developed model will be a foundation for broader applications in future studies, including integrating coupled systems.\u003c/p\u003e \u003cp\u003eThe ArcSWAT 2012.10_8.25 software, compatible with ArcMap 1.8.2, was used to simulate the SWAT model for the PRW over 20 years (2000\u0026ndash;2020) using pre-processed spatial and climate data. SWAT-CUP with the SUFI-2 algorithm (Abbaspour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khalid et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was employed for uncertainty analysis, calibration, and validation, noted for its efficiency in handling large-scale models (Pandey et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). 16 sensitive SWAT parameters (Anaba et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were considered during the uncertainty analysis (Abbaspour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The model was calibrated for river discharge from 2003 to 2014 and validated from 2015 to 2021 using the most sensitive parameters fitted values, ensuring reliability through uncertainty analysis and performance evaluation. Model performance was evaluated using statistical parameters, including NSE, PBIAS, R\u003csup\u003e2\u003c/sup\u003e, and RMSE, with sensitivity indicated by t-stat and p-values.\u003c/p\u003e \u003cp\u003eIn the uncertainty analysis, out of 16 identified sensitive SWAT parameters, four were revealed to be highly sensitive (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The baseflow factor ratio, SCS Runoff Curve Number for moisture condition II, Effective hydraulic conductivity, and moisture bulk density remain highly sensitive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00), affecting the comparative analysis between simulated and observed RiverQ. Singson et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also identified these sensitive parameters as sensitive parameters and related studies. Their sensitivity is rooted directly in their direct influence on critical hydrological processes (Araza et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), such as surface runoff, infiltration, baseflow, and soil-water interactions, essential for accurately simulating RiverQ and GWR in PRW.\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\u003eSensitivity Analysis of SWAT Parameters with Corresponding through Global Sensitivity Analysis (Jimenez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\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\" colname=\"c1\"\u003e \u003cp\u003eSWAT Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Stat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValue Range\u003c/p\u003e \u003cp\u003e(Anaba et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFitted (Optimal) Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. V_ALPHA_BNK.rte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseflow alpha factor ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. R_CN2.mgt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCS Runoff Curve Number for moisture condition II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-14.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026ndash;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. R_SOL_K().sol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective hydraulic conductivity (mm/hr)\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\u003e-5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. R_SOL_BD().sol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist bulk density (g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\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\u003eAfterward, the fitted values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) of the identified sensitive parameters yielded satisfactory calibration and validation results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). During the calibration period (2003\u0026ndash;2014), the model achieved an NSE of 0.57, indicating that the model captured 57% of the variability in river discharge compared to observed values. An R\u0026sup2; of 0.66 shows a moderate correlation between simulated and observed discharge, confirming the model\u0026rsquo;s ability to replicate trends in streamflow. The PBIAS of 2.80% indicates a minimal overestimation, which is acceptable within hydrological modeling standards (Moriasi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the validation period (2015\u0026ndash;2021), the model performance remained satisfactory with an NSE of 0.54, an R\u0026sup2; of 0.71, and a PBIAS of 14.0%, although there was a slight decrease in NSE and an increase in PBIAS.\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\u003eSWAT model calibration and validation evaluation statistics (Jimenez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\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\u003eSTATISTICAL PARAMETERS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCALIBRATION PERIOD (2003\u0026ndash;2014)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVALIDATION PERIOD (2015\u0026ndash;2021)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBSERVED RiverQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSIMULATED RiverQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOBSERVED RiverQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSIMULATED RiverQ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEAN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eST DEV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePBIAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRSR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.68\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\u003eThe slight decline in NSE and rise in PBIAS can be attributed to extreme discharge misestimations commonly observed in SWAT simulation studies (Singson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NSE reductions indicate that the model\u0026rsquo;s ability to capture variability in extreme events has limitations, particularly during high-flow periods. PBIAS increases suggest that the model tends to overestimate streamflow, particularly in extreme months, which is consistent with findings from Du et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Alejo et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who emphasized the need for local validation and multi-dataset comparisons to improve data accuracy in hydrological models. These results underscore the importance of interpreting the model\u0026rsquo;s outputs based on trends rather than absolute values to ensure reliable insights, particularly when assessing extreme hydrological events in data-scarce regions. With the increasing impact of climate variability, the Padsan River Watershed (PRW) has experienced significant changes in extreme events, necessitating further validation and refinement of hydrological models to account for these variabilities. More discussions on the works of Jimenez et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eClimate Change Model Selection and Scenario Building\u003c/h3\u003e\n\u003cp\u003eClimate change scenarios are essential for understanding future climatic conditions and their impacts on hydrological processes. These scenarios project changes in temperature and precipitation under various greenhouse gas emissions and socioeconomic pathways, providing projections for assessing potential impacts on water resources and ecosystems. The study utilized the Couple Model Intercomparison Project Phase 6 (CMIP6), which offers advanced climate simulations with improved model resolution and representation of physical processes compared to the previous version (CMIP5), offering reliable projections for hydrological studies (Eyring et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo ensure consistency and robustness, the study adopts the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework, specifically the ISIMIP3b version, which integrates CMIP6 projections to downscaled and bias-corrected climate data (Lange \u0026amp; B\u0026uuml;chner \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ISIMIP framework ensures that the climate projections are standardized and comparable, supporting cross-sectoral assessments of climate impacts on water resources (Volkholz et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study extracted provincial-level downscaled monthly relative change data for precipitation and temperature from the ClimoCast platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://a-plat.nies.go.jp/ap-plat/cmip6/global.html\u003c/span\u003e\u003cspan address=\"https://a-plat.nies.go.jp/ap-plat/cmip6/global.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to represent the mean monthly changes in the PRW. The ClimoCast platform serves as the data provider for the ISIMIP3b (Lange \u0026amp; B\u0026uuml;chner \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ISIMIP3b data were sourced from 10 CMIP6 models, categorized into periods: Current Future (CF: 2025\u0026ndash;2040), Near Future (NF: 2041\u0026ndash;2060), Middle Future (MF: 2061\u0026ndash;2080) and Far Future (FF: 2081\u0026ndash;2100). The use of downscaled climate data from ISIMIP3b ensures high-resolution projections with bias correction, improving the accuracy of hydrological modeling for RiverQ and GWR in the PRW.\u003c/p\u003e \u003cp\u003eThis study considers climate projections from 10 models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) based on the latest CMIP6 data. These model are internationally recognized for their robust simulation of climate processes and have been validated through historical performance assessment. These models composed of 5 primary model that includes GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2HR, MRI-ESM2-0 and UKESM1-0-LL while the secondary model are CanESM5, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth, and MIROC6 (Lange, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These four climate scenarios (SSP126, SSP245, SSP3370, SSP585) (O\u0026rsquo;Neill et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) represent different future greenhouse gas (GHG) emissions pathways for the hydrologic impact simulations, providing the derive insight for broader applications and efficient information.\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\u003eSelected 10 Climate Models utilized for the study\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\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtended Model Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM-CM6-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentre National de Recherches M\u0026eacute;t\u0026eacute;orologiques Climate Model version 6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Voldoire et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNRM-ESM2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentre National de Recherches M\u0026eacute;t\u0026eacute;orologiques Earth System Model version 2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (S\u0026eacute;f\u0026eacute;rian et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanadian Earth System Model version 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Swart et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean Consortium Earth System Model version 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (D\u0026ouml;scher et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeophysical Fluid Dynamics Laboratory Earth System Model version 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Dunne et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInstitut Pierre-Simon Laplace Climate Model version 6A Low Resolution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (g et al., 2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel for Interdisciplinary Research on Climate version 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapan, Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Shiogama et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax Planck Institute Earth System Model version 1.2 High Resolution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (M\u0026uuml;ller et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeteorological Research Institute Earth System Model version 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapan, Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Yukimoto et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUKESM1-0-LL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeteorological Research Institute Earth System Model version 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource: (Tang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e* 1 \u0026ndash; 5 is primary, 6 \u0026ndash; 10 is secondary models\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBy using state-of-the-art climate from CMIP6 and ISIMIP3b, the study ensures a comprehensive and consistent impact assessment, providing reliable insights for policy and decision-making regarding climate resilience in conjunctive water resources management.\u003c/p\u003e\n\u003ch3\u003eClimate Change Impact Simulation and Output Visualization\u003c/h3\u003e\n\u003cp\u003eThe calibrated and validated model simulated the watershed hydrology from 2000 to 2021, including a three-year warming period (2000 to 2002) to establish a baseline for the simulation. This baseline supports the evaluation of climate change scenarios using the Shared Socioeconomic Pathways (SSPs) framework available from the ClimoCast data provider. SSP1 envisions a sustainable future with a shift toward inclusive development and environmental awareness, prioritizing education, health, and clean technology investments to reduce inequalities and enhance climate resilience. SSP2, labeled \"Middle of the Road,\" describes a world where current trends continue, leading to moderate progress in sustainability with uneven regional development and persistent social inequalities. SSP3, known as \"Regional Rivalry (A Rocky Road),\" portrays a fragmented world focusing on regional interests, resulting in strong national identities, limited international cooperation, and weak environmental initiatives. SSP4, termed \"Inequality (A Road Divided),\" depicts a scenario of pronounced inequalities, where a technological elite thrives while the majority remains marginalized, leading to low social cohesion and significant environmental degradation. Finally, SSP5, \"Fossil-Fueled Development (Taking the Highway),\" illustrates a world characterized by rapid economic and technological growth driven by fossil fuels, resulting in high emissions but improved adaptive capacity through economic gains. These scenarios provide a comprehensive framework for quantifying the impacts of climate change on river discharge and groundwater recharge, as detailed by O\u0026rsquo;Neill et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study aimed to utilize these diverse pathways to understand the potential impact of future environmental changes and inform effective policy strategies for managing water resources under varying socioeconomic conditions.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of validated CHIRPS and other Open-access Model Data Inputs\u003c/h2\u003e \u003cp\u003eThe fragmented management of data in the watershed presents challenges in delivering rapid and robust strategies for preserving water resources in the context of climate change and depleting resources. However, the increasing availability of open-access satellite data (such as CHIRPS) and high-resolution, bias-corrected CMIP6 climate projections offer opportunities to address those gaps. Similar to several studies highlighting the works of Alejo, Ella, \u0026amp; Saludes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the use of CHIRP as rainfall input for SWAT watershed modeling in various climate types in the Philippines (Alejo \u0026amp; Alejandro, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), achieving satisfactory calibration and validation results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, CHIRPS performance varies across regions and requires local validation and bias correction to improve its reliability for hydrological modeling (Du et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By integrating bias-corrected CHIRPS data and other open-access datasets into the SWAT model, this study achieved satisfactory performance in simulating river discharge and assumed to satisfactorily simulate the groundwater recharge, demonstrating the model\u0026rsquo;s potential to provide scientific insights for conjunctive surface and subsurface water management, particularly in data-scarce regions, like PRW.\u003c/p\u003e \u003cp\u003eMoreover, the calibrated and validated SWAT model supports the study\u0026rsquo;s claims that it can accurately simulate river discharge (RiverQ) and groundwater recharge (GWR) in the Padsan River Watershed (PRW). The model effectively captures RiverQ variability, which aligns with the precipitation patterns observed in the CHIRPS dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, extreme hydrological events were overestimated, consistent with the findings of Du et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who highlighted variability in CHIRPS performance across regions and its limitations in capturing extreme precipitation events while mentioning its strength in representing extremely low conditions. Similarly, Alejo et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) noted that CHIRPS may misrepresent localized rainfall patterns, particularly during high-intensity rainfall events, which can lead to biases in hydrological simulations. These discrepancies suggest that applying the study\u0026rsquo;s findings to other contexts requires additional validation and analysis, particularly for extreme hydrological conditions, to ensure accurate and reliable applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClimate Change Projections in the PRW\u003c/h3\u003e\n\u003cp\u003eThe monthly relative change of precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was extracted from the ISIMIP3b framework through the ClimoCast platform. Time series divided into four future timeframes. All scenarios indicate fluctuating rainfall patterns, with the 2030s showing minor variability while variability increases by the 2090s, which makes the projected impact of greenhouse gas emissions distinct.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, SSP126 shows moderate fluctuations in a low-emission scenario, indicating stable precipitation patterns. In contrast, SSP245 and SSP337 display intermediate to increasing variability along with higher timeframe. At the same time, SSP585, the high-emission scenario, exhibits extreme rainfall variations, with a pronounced peak by the 2090s, highlighting the potential for severe hydrological impacts.\u003c/p\u003e \u003cp\u003eThese findings correspond to the highlights provided by Tebaldi et al., (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which could lead to a heightened risk of extreme rainfall events that could lead to more frequent and severe flooding due to higher river flow while significantly altering groundwater recharge. The anticipated escalation in rainfall variability underscores the necessity for adaptive water resources management strategies that could mitigate the adverse effects on groundwater recharge cycles and river discharge patterns, enhancing the climate resilience of the watershed. This growing climate variability over time enables Philippine policies, e.g., the Integrated Water Resources Management Plan (IWRMP), to recommend actions with strict timelines for sustainable water use (Department of Environment and Natural Resources [DENR], \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProjected Impact of Climate Change on River Discharge\u003c/h2\u003e \u003cp\u003eThe calibrated and validated SWAT model simulated future river discharge in the Padsan River Watershed under various climate change scenarios, comparing these projections with historical discharge data from 2000 to 2020. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e (Top Line Graph) shows that the historical river discharge displays seasonal fluctuations driven by annual precipitation cycles. From 2020 onward, all SSP scenarios predict an increase in river discharge, with higher-emission pathways such as SSP585 and SSP245 showing more substantial increases. The peaks in discharge are more pronounced under SSP585, reflecting more frequent and intense precipitation events, whereas SSP126 shows a moderate increase, closer to historical values, indicating that lower emissions result in less drastic changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relative changes in river discharge compared to the historical baseline (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e) show a consistent upward trend across all SSP scenarios. SSP585 exhibits the steepest increase, indicating more extreme shifts in water flow over time. Even under low-emission pathways like SSP126, the gradual rise in discharge highlights that future river flow will exceed historical norms, with relative changes ranging from 116\u0026ndash;119%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean Relative Changes of Net River Discharge of the Watershed\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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCC Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRelative Change (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAbsolute Change (depth, mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u0026ndash;2020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e723.77\u0026thinsp;\u0026plusmn;\u0026thinsp;136.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e569.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,003.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e866.83\u0026thinsp;\u0026plusmn;\u0026thinsp;143.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e674.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,174.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e868.59\u0026thinsp;\u0026plusmn;\u0026thinsp;142.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e659.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,167.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e844.76\u0026thinsp;\u0026plusmn;\u0026thinsp;142.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e643.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,164.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e866.72\u0026thinsp;\u0026plusmn;\u0026thinsp;144.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e655.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,187.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*Historical river discharge\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the average net river discharge values (net and absolute) and each scenario's standard deviation for four future decades (2020 to 2100). High river discharge averaged around 723.77 m/s\u003csup\u003e3\u003c/sup\u003e, while the projected future discharge substantially increased across all SSP scenarios. The average discharge ranges from 844.75 m/s\u003csup\u003e3\u003c/sup\u003e under SSP370 to 868.83 m/s\u003csup\u003e3\u003c/sup\u003e under SSP126, reflecting the overall increase in water flow at the watershed outlet regardless of the emission pathways. Notably, the standard deviation remains relatively stable across all scenarios, indicating that while the absolute discharge values increase, the discharge variability (or fluctuation) is consistent with historical patterns. This suggests that while more water will flow through the watershed on average, the seasonal fluctuations and interannual variability will remain within a similar range, though at higher discharge levels. The relatively stable standard deviation values also imply that the watershed will continue to experience predictable seasonal cycles. Still, these will occur at a higher baseline discharge, particularly in higher-emission scenarios. The average relative changes in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e support these claims. The average relative change remains consistent across all scenarios, with values ranging between 116% (SSP370) and 119% (SSP126 and SSP585). These relative changes indicate that river discharge will be approximately one-fourth higher than the historical baseline on average across the projection period. While the increase is relatively consistent, the standard deviation values remain predictable. SSP126 and SSP585 exhibit slightly higher averages than SSP245 and SSP370, suggesting that the lowest and highest emission scenarios will lead to more significant relative discharge increases than the medium-emission scenarios. The overall predictability of relative changes increases in river discharge over time, driven by climate change.\u003c/p\u003e \u003cp\u003eThese findings that capture the impact of CMIP6 on river discharge of PRW is comparable with the findings by Tolentino et al., (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) that generally concluded a general increase of water flow for most Philippine watersheds. Similar to the findings of (Jimenez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the impact of climate change disrupts the seasonal pattern of discharge, affecting water availability. This comparison potentially aggravated current drought (Alonzo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and flooding (Graciela et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) events in the watershed and the province. On the other hand, the findings are comparable to the improvement of previous studies, which use the previous version of climate change. CMIP6 projects a broader range of future climate outcomes with higher radiative forcing scenarios and increased climate sensitivities leading to greater projections (Tebaldi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These boost its significant application in the watershed, that is highly vulnerable to flow variability due to its geographical location and challenging adaptation measures (Ignacio-Reardon \u0026amp; Luo, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProjected Impact of Climate Change on Groundwater Recharge\u003c/h2\u003e \u003cp\u003eConsidering the increasing importance of subsurface resources in the face of climate change, the fragmented institutional management (Valenzuela \u0026amp; Gutierrez, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) leads to scarce frameworks that challenge adaptation measures (Ignacio-Reardon \u0026amp; Luo, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for the watershed. With this data scarcity in the watershed management, the reliance on the process-based model to provide the foundation in establishing governance of the subsurface resources recharge in the face of climate variability is essential, highly recommending the significant contribution of consolidated data management for institutions.\u003c/p\u003e \u003cp\u003eThe calibrated SWAT model simulated groundwater recharge from 2020 to 2100 under four SSP scenarios. The general trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) shows periodic fluctuations in recharge driven by seasonal variability and a long-term increase across all scenarios. Higher emission scenarios (SSP585 and SSP245) exhibit steeper trends, indicating more significant increases in recharge rates, while SSP126 and SSP370 show moderate increases. This suggests that climate change, particularly under high-emission pathways, will lead to increased groundwater recharge, primarily due to intense precipitation events that may pose flooding risks rather than contributing to sustainable groundwater replenishment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the relative changes in mm/mm are also plotted in the projected futures. The relative changes highlight that SSP126 has the highest variability, with peaks reaching nearly 0.6, especially towards the latter half of the century. The climate low emission projection has the highest mean relative change (27.68%) over the rest of the 21st century (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which can fluctuate more compared with other climate change scenarios, as evidenced by the standard deviation to mean (\u0026plusmn;\u0026thinsp;11.42). It can increase from 12.21\u0026ndash;64.34% from the current annual recharge, resulting in an estimated average annual groundwater change of 116.01\u0026thinsp;\u0026plusmn;\u0026thinsp;218.79 mm. SSP245. Compared to SSP126, the other three climate change scenarios exhibit more stable relative changes, with SSP245 and SSP370 staying consistently lower. SSP245, the moderate emission, posts a relative change to the groundwater recharge of 27.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98%, which can increase the average annual groundwater recharge by 20.86\u0026ndash;39.13%. This resulted in a 70.95 mm to 153.32 increase in the watershed groundwater recharge. SSP370, considered a high emission, has the lowest fluctuation of changes. The average relative change is 24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98%, which resulted in an increase of 57.49 to 134.20 (mean of 103.28 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;134.20) from the current annual groundwater recharge. While the high emission had the lowest average relative change, SSP585 was labeled as the extreme (or very high) emission, followed closely by SSP 245, having a relative change of 27.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96% with lesser fluctuations ranging from a minimum of 18\u0026ndash;40.13%. In response, the projected increase in the current annual groundwater recharge was 77.59 mm to 157.23 mm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual Watershed Relative and Absolute Groundwater Recharge of LRW.\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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCC Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRelative Change (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAbsolute Change (depth, mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.01\u0026thinsp;\u0026plusmn;\u0026thinsp;218.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e218.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.51\u0026thinsp;\u0026plusmn;\u0026thinsp;153.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.28\u0026thinsp;\u0026plusmn;\u0026thinsp;134.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e134.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.70\u0026thinsp;\u0026plusmn;\u0026thinsp;157.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e157.23\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\u003eThe relative changes reflect significant deviations from the baseline recharge, driven by precipitation variability under climate forcing. The response of groundwater recharge rate on lower emissions exhibits extreme sensitivity to rainfall shifts compared to higher emissions with stable recharge rates within a narrower range. These findings highlight the groundwater recharge response to natural variability (Garcia-Menendez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) compared to higher emissions with enhanced human-induced forcing. On the other hand, all SSPs indicate an increasing groundwater recharge rate, indicating the potential increase of the groundwater supply that can serve as a buffer of the increasing variability of climate, considering the sustainable extraction by incorporating the variability across time and space in the PRW, that could be implemented with thorough governing policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengthening National Policies with Data-Driven Hydrological Changes\u003c/h2\u003e \u003cp\u003eThe Philippines is taking a significant step toward sustainable water management with the proposed Department of Water Resources (National Water Resources Management Act S. No. 2771 19th Congress, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), addressing long-standing government fragmentation resulting in poor data management. The Philippines Development Plan (2023\u0026ndash;2028; National Economic Development Authority [NEDA], 2023) highlights the need for science-based strategies, integrating hydrological models and satellite data to improve water planning and resilience.\u003c/p\u003e \u003cp\u003eWith climate variability complicating conjunctive water management, the PDP calls for Integrated Water Resource Management (IWRM) and ecosystem-based approaches (NEDA, 2023). Using the SWAT model reinforces the need for conjunctive water use planning by determining how erratic rainfall impacts river discharge and groundwater recharge. Frequent extreme weather events threaten water availability and infrastructure, making flood protection, water retention, and aquifer recharge strategies crucial. This study proposes an integrated conjunctive simulation framework, combining SWAT and satellite data, to enhance climate resilience. Aligning with policies, it supports climate-adjusted planning and conversation measures such as climate-smart farming, deforestation, and artificial recharge in PRW.\u003c/p\u003e \u003cp\u003eThe PDP also underscores the need for centralized water management to streamline policies and improve resilience (NEDA, 2023). This study supports investing in infrastructure rehabilitation and new projects to address shifting hydrological patterns. While the PDP warns against groundwater overexploit, this study finds that groundwater recharge increases under future climate scenarios. With proper governance, including artificial recharge initiatives, groundwater reservoirs can be a crucial buffer during dry periods.\u003c/p\u003e \u003cp\u003eScience-driven water management is essential for informed decision-making. Hydrological simulations provide reliable data for monitoring watershed, ensuring sustainability policies for surface and groundwater use. The PDP\u0026rsquo;s emphasis on conjunctive management aligns with the study\u0026rsquo;s findings \u0026ndash; enhancing groundwater recharge benefits communities, ecosystems, and long-term adaptation.\u003c/p\u003e \u003cp\u003eFinally, this study highlights the need to protect and restore aquifer recharge zones, reinforcing PDP goals of safeguarding watersheds. Integrating data-driven hydrological modeling into national policies will strengthen water management and ensure sustainable water resources for future generations.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe SWAT model performed satisfactorily during the calibration (NSE\u0026thinsp;=\u0026thinsp;0.57, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.66) and validation (NSE\u0026thinsp;=\u0026thinsp;0.54, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.71). Slight misinterpretations of extreme weather events were recorded due to the climatic conditions of the watershed. Nevertheless, the satisfactory performance provides reliable insights into strengthening national policies and governance of the PRW and other places with similar watershed settings. The accessibility of five primary and five secondary CMIP6 climate projections gave an avenue to understand the projected impact of climate variability on River Discharge and Groundwater Recharge. Furthermore, these bias-corrected and downscaled climate models for projections allow the study to understand the temporal distribution for four futures in four SSPs. Results show that all SSPs will enhance rainfall variability, potentially disrupting river discharge and groundwater recharge current pattern threatening infrastructures and crop productions due to extreme climate conditions (floodings and droughts). The annual river discharge could increase by a minimum of 108.76% to a maximum increase of 134.25% across SSPs and timelines. Groundwater recharge could also increase by a minimum of 12.21% and a maximum of 40.13% across SSPs and timelines. These results supported PDP\u0026rsquo;s claims that integrated water resources management can capture excess water. The PDP also promotes the establishment of water infrastructure to combat droughts and reassess existing water infrastructure that could easily be disrupted due to the high variability of river discharge. Thus, the result of this study showcases the benefit of a data-driven integrated framework approach to strengthen the PRW policies and governance that could promote sustainability across watershed sectors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Grammarly to improve the conciseness, flow, and writing tone of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take(s) full responsibility for the publication\u0026apos;s content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; The authors would like to acknowledge the Department of Science and Technology \u0026ndash; Science and Education Institute, Philippines, which provided research funds for the PhD Dissertation. Gratitude is also given to the Asian Institute of Technology, Thailand, and the Indian Institute of Technology \u0026ndash; Roorkee, India, for providing laboratories to conduct the simulations. Other institutes worth noting are the Mariano Marcos State University and National Irrigation Administration \u0026ndash; San Nicolas, Ilocos Norte, for providing ground-based data that was used to calibrate and validate the numerical model. Finally, most importantly, the Almighty provided strength and motivation to accomplish this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbaspour, K. C., Vaghefi, S., \u0026amp; Srinivasan, R. (2017). A guideline for successful calibration and uncertainty analysis for soil and water assessment: A review of papers from the 2016 international SWAT conference. In \u003cem\u003eWater (Switzerland)\u003c/em\u003e (Vol. 10, Issue 1). MDPI AG. https://doi.org/10.3390/w10010006 \u003c/li\u003e\n\u003cli\u003eAlejo, L. A., \u0026amp; Alejandro, A. S. (2021). Validating CHIRPS ability to estimate rainfall amount and detect rainfall occurrences in the Philippines. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e145\u003c/em\u003e(3\u0026ndash;4), 967\u0026ndash;977. https://doi.org/10.1007/S00704-021-03685-Y/TABLES/5 \u003c/li\u003e\n\u003cli\u003eAlejo, L. A., \u0026amp; Alejandro, A. S. (2022). 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The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component. \u003cem\u003eJournal of the Meteorological Society of Japan. Ser. II\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e(5), 931\u0026ndash;965. https://doi.org/10.2151/JMSJ.2019-051\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate Change, Geospatial Data, Groundwater Recharge, Quantile Mapping, River Discharge, CHIRPS, CIMP6","lastPublishedDoi":"10.21203/rs.3.rs-6333077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6333077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn previous years, water is abundant in the Padsan River Watershed, even in tropical climates. However, with increasing variability due to global warming, the watershed faces disruption of water resources. This requires thorough study, yet with fragmented data management, it becomes challenging. With technological advantages, integrated water resources management (IWRM) becomes possible using open-access data to understand the potential impact of climate variability. The corrected Climate Hazards Group InfraRed Precipitation (CHIRPS) and ten Coupled Model Intercomparison Project Phase 6 (CMIP6) becomes the notable open-access data including request-based institutional data were used as inputs for the Soil and Water Assessment Tools (SWAT) to quantify the impact of climate change on river discharge and groundwater recharge. Results showed that all Shared Socioeconomic Pathways (SSPs) will disrupt river discharge and groundwater recharge, with a prominent increase in river discharge. Furthermore, SSP 585 in the 2090s has a more notable impact on river discharge than others. In contrast, the SSP 126 has a lesser impact but displays higher variability across the rest of the century. This important simulated observation highly supports the Philippine Development Plan (PDP) aims, which is that climate change's impact could disrupt existing infrastructure and recharge conservation while establishing recharge areas to combat water scarcity during the dry period in the watershed. In contrast to the PDP regarding groundwater use, the study also supports the increasing conjunctive use of surface and subsurface resources, given that comprehensive management of the subsurface extraction must be established based on the study\u0026rsquo;s results.\u003c/p\u003e","manuscriptTitle":"Quantified Impact of Projected Climate Change on Groundwater Recharge and River Discharge Leveraging the Use of Open Access Geospatial Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 13:51:06","doi":"10.21203/rs.3.rs-6333077/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21f35f6e-2fb1-41ac-9c74-17c9302cce61","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T19:40:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 13:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6333077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6333077","identity":"rs-6333077","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}