Next Article in Journal
Circular Economy in the Construction Sector in Materials, Processes, and Case Studies: Research Review
Previous Article in Journal
Exploring the Role of Industry 4.0 Technologies in Smart City Evolution: A Literature-Based Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hydrological Modeling of the Chikugo River Basin Using SWAT: Insights into Water Balance and Seasonal Variability

1
Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8521, Japan
2
Department of Environmental Science and Technology, University of Science and Technology of Southern Philippines, Cagayan de Oro City 9000, Philippines
3
Japan Forest Technology Association, Tokyo 105-0001, Japan
4
PASCO Corporation, Tokyo 153-0043, Japan
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 7027; https://doi.org/10.3390/su17157027 (registering DOI)
Submission received: 6 June 2025 / Revised: 26 July 2025 / Accepted: 28 July 2025 / Published: 2 August 2025

Abstract

Integrated hydrological modeling plays a crucial role in advancing sustainable water resource management, particularly in regions facing seasonal and extreme precipitation events. However, comprehensive studies that assess hydrological variability in temperate river basins remain limited. This study addresses this gap by evaluating the performance of the Soil and Water Assessment Tool (SWAT) in simulating streamflow, water balance, and seasonal hydrological dynamics in the Chikugo River Basin, Kyushu Island, Japan. The basin, originating from Mount Aso and draining into the Ariake Sea, is subject to frequent typhoons and intense rainfall, making it a critical case for sustainable water governance. Using the Sequential Uncertainty Fitting Version 2 (SUFI-2) approach, we calibrated the SWAT model over the period 20072–2021. Water balance analysis revealed that baseflow plays dominant roles in basin hydrology which is essential for agricultural and domestic water needs by providing a stable groundwater contribution despite increasing precipitation and varying water demand. These findings contribute to a deeper understanding of hydrological behavior in temperate catchments and offer a scientific foundation for sustainable water allocation, planning, and climate resilience strategies.

1. Introduction

Water resources are not only fundamental to human survival and agricultural productivity, but also for the health of marine ecosystems and industrial development. However, their availability is increasingly constrained by growing demands and environmental changes [1]. Hydrology, particularly the study of water balance and rainfall-runoff processes, is pivotal in effective water resource management, flood mitigation, and environmental planning [2]. However, these processes are under significant stress due to anthropogenic activities and environmental factors. Global climate change, extreme weather events, deforestation, urban expansion, and intensified agricultural practices have disrupted natural hydrological cycles, reducing groundwater replenishment, increasing surface runoff, and causing frequent floods and droughts [3]. These disruptions seriously threaten water security, particularly in regions with unpredictable or seasonal rainfall patterns, where fluctuations in water availability can exacerbate food insecurity and hinder sustainable development [4,5]. Furthermore, altered freshwater flows can have far-reaching effects downstream, impacting estuarine and marine ecosystems through changes in salinity, nutrient transport, and sediment delivery, ultimately influencing coastal biodiversity and ecological resilience. To address these challenges, a comprehensive understanding of hydrological processes is essential. This involves evaluating watershed characteristics through quantitative assessments of inputs (e.g., precipitation), outputs (e.g., runoff and evapotranspiration), and changes in water storage [6]. Such analyses are particularly critical in arid and semi-arid regions, where hydrological events are highly variable and heavily influenced by irregular precipitation patterns. However, even in temperate regions, such as the Chikugo River catchment in Japan, climate variability, rapid urbanization, and intensive agricultural practices have created complex hydrological dynamics that require urgent attention.
Streamflow modeling is a vital tool for addressing these issues because it supports reservoir management, water distribution planning, and flood and drought mitigation. Several hydrological models have been developed to simulate streamflow, including the Soil and Water Assessment Tool (SWAT), Python TOPographic Kinematic AP-proximation and Integration (PyTOPKAPI), and Hydrologic Modeling System-Hydrologic Engineering Center (HEC-HMS) [7,8,9]. Among the available hydrological models, the Soil and Water Assessment Tool (SWAT) stands out for its versatility, robustness, and broad applicability across diverse river basins and environmental settings [10,11,12]. Its strength lies in simulating key hydrological processes such as precipitation, runoff, evapotranspiration, and groundwater recharge while accounting for the effects of climate change, land use alterations, and urban development on watershed dynamics through integration with Geographic Information Systems (GIS) [13].
Previous studies in the Chikugo River Basin have primarily focused on flood flow analysis and pollutant load estimation using hydrological models such as HEC-HMS. For example, a study conducted by [14] simulated rainfall runoff and pollutant loads using a GIS-based distributed parameter model, emphasizing water quality indicators like total nitrogen and phosphorus. While valuable for watershed management, this study did not explore temporal and seasonal hydrological variability or the integration of water balance components within the context of current water demand and supply, which is essential for long-term planning. While GIS-based approaches have increasingly enhanced hydrological analysis at multiple scales in complex catchments, their application in the Chikugo catchment remains limited [15]. To address these gaps, this present study applies the SWAT model to the Chikugo River catchment, aiming to generate a comprehensive understanding of its hydrological behavior, especially focusing on baseflow or groundwater availability, which is essential to domestic and agricultural water demand management. Specifically, the study aims to (i) quantify the annual and seasonal key water balance components—evapotranspiration, surface runoff, baseflow, and recharge to the deep aquifer; (ii) assess factors contributing to water balance distribution; and (iii) estimate water availability across the study period (2007–2019) in the Chikugo catchment.
This approach addresses persistent knowledge gaps in integrated hydrological modeling for complex and data-scarce catchments such as the Chikugo River Basin, offering actionable insights for sustainable water resource management amid increasing urbanization and climate pressures. This enhanced understanding supports efforts to manage water availability and quality more effectively, particularly in regions where urban expansion poses risks to transboundary water security. Moreover, this analysis can be used in other catchments with similar environmental and data challenges. It helps in building practical and scalable plans for managing water resources in response to a changing climate and land use.

2. Materials and Methods

2.1. Study Area

The Chikugo River basin (Figure 1), encompassing approximately 2860 km2 with an elevation ranging from 0 m above sea level (masl) in the downstream portion to 1524 masl in the upstream mountainous areas, is located in northern Kyushu Island, Japan. It extends across Fukuoka, Saga, Kumamoto, and Oita Prefectures, with most of the basin area located in Fukuoka and Saga. The Chikugo River, with a total length of 143 km, is the longest river on Kyushu Island. It originates from Mount Aso and discharges its water into the Ariake Sea. The upper reaches of the river are important for forestry, whereas the middle and lower reaches are crucial for local agriculture, providing irrigation water for 400 km2 of rice fields on the Tsukushi Plain. The river is also vital for industry, with 20 electrical power plants situated along its banks, and is the major city of Kurume in Fukuoka Prefecture, thus contributing to the local economic sustainability of the study area. The mean freshwater inflow into the estuary is 54 m3/s during the dry season, and it exceeds 2800 m3/s during the rainy season [16]. The annual mean precipitation in the watershed is 2180 mm, reaching a maximum of 3000 mm in the mountainous areas.
The Chikugo River estuary is one of the most productive aquatic systems in Japan and supports numerous semi-endemic species [17]. The Chikugo River catchment, a vital water resource for the Fukuoka metropolitan area, faces significant challenges due to population growth, economic development, and climate variability. Industrialization and modernization have intensified water usage, necessitating careful planning to balance the competing demands of domestic, industrial, and agricultural consumers [18]. Historically, the region has experienced severe droughts caused by unprecedentedly low rainfall events, such as those in 1978 and 1994. More recently, abnormally low rainfall during critical periods, such as the autumn of 1999 and 2006 and the rainy seasons of 2002 and 2005, has led to sharp declines in the water volume of the Chikugo River in Senoshita station, prompting the implementation of drought countermeasures. The basin has also experienced extreme runoff events, such as the high historical water levels recorded at the Senoshita station in 2020, highlighting the region’s vulnerability to both floods and droughts.
Between 2006 and 2021, the Chikugo catchment experienced significant land use transformation, marked by rapid urban expansion and a corresponding decline in natural vegetation, as illustrated in Figure 2. Built-up areas nearly doubled from 231.4 km2 (8.7%) to 456.3 km2 (17.1%), while agricultural paddy fields and evergreen forests declined by 260.3 km2 (−9.7%) and 190.6 km2 (−7.1%), respectively [Table S5]. These changes reflect broader regional trends in Japan, where urban growth is driven by infrastructure development, rural depopulation, and the consolidation of urban centers. In the Chikugo catchment, this urban expansion primarily occurred at the expense of agricultural and forested lands, particularly evergreen forests, which are ecologically significant for their high-water retention and transpiration capacities.

2.2. General Data Processing

The Q-GIS 3.16 was used as an interface to run the SWAT 2012 model. In this study, key input data, including digital elevation model (DEM), soil, land use, and weather data, were processed using QSWAT to define the watershed, create hydrological response units (HRUs), and generate input files [Figure S2]. A 30-m resolution DEM from the United States Geological Survey (USGS) Shuttle Radar Topography Mission (SRTM) was used to delineate subbasins and stream networks. The soil map (Figure 1b) shows 12 soil types, including Andosols, Cambisols, Acrisols, and Gleysols as dominant soils [Table S4]. The land-use map (Figure 1c) shows that forests dominate the catchment (57.47%), primarily in the upper and midstream regions, whereas agricultural land (27%) is concentrated in lowland areas. Urban areas (10.41%) are mainly downstream, with the remaining 7% comprising grasslands, infrastructure, and water bodies. Slope classes were categorized into five ranges (0–10%, 10–20%, 20–30%, 30–45%, and >45%) to account for variations in runoff and erosion potential. The catchment was divided into 28 subbasins and 3151 HRUs. Land-use and soil data were obtained from the Ministry of Land, Infrastructure, and Transport (MLIT), whereas daily weather data (2004–2021) from 13 meteorological stations [Table S2] were sourced from the Japan Meteorological Agency (JMA). Streamflow data from the Senochita station (2004–2021) were used for calibration and validation and accessed via the MLIT water information system (Table 1).

2.3. Model Performance and Sensitivity Analysis

Model calibration and validation were conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2) in the SWAT-CUP. The initial parameter ranges were defined based on the SWAT user manual and refined through iterative optimization. SUFI-2 employs a stochastic approach, running simulations per iteration to minimize errors and assess parameter uncertainty [24]. Calibration was primarily automatic, with manual adjustments made to avoid overfitting and ensure physical relevance. The simulation period ran from 2004 to 2021 and included a warming period (2004–2006). The calibration phase occurred between 2007 and 2014, and the validation phase lasted from 2015 to 2021. These two durations were selected based on the average daily observed streamflows during the two periods, which did not differ significantly. The SWAT model in this study replicated the rainfall-runoff connection using the modified Soil Conservation Service (SCS) curve number technique. Given the limited meteorological data in the research area, we employed the Hargreaves potential evapotranspiration (PET) approach, which is appropriate for areas with limited meteorological data [25]. To avoid over-parameterization and to determine the sensitive parameters, we performed a sensitivity analysis for several parameters that control the hydrological processes in the SWAT prior to the calibration phase [26]. In this study, we ran 100 simulations per iteration, and an uncertainty analysis was conducted together with calibration. The optimization process of the sensitivity analysis of the flow parameters is determined by multiple regression methods against the objective function that reverts the Latin hypercube-created parameters [24]. Approximately 17 parameters related to management, soil, and groundwater were tested using the absolute parameter range provided in the SWAT CUP and then ranked according to their sensitivity based on t-value and p-values. The model parameter with the largest t-distribution (i.e., the most sensitive) and the lowest p-value (i.e., the most significant) was assigned. Here, the parameter was significant if the p-values were close to zero. Subsequently, the best-fit value of each parameter was run once in the validation period. Model performance was evaluated using the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and percentage bias (PBIAS). Satisfactory model performance was achieved, with R2 and NSE values above 0.80 and PBIAS within acceptable ranges during both calibration and validation [27]. The formula for the three parameters (Equations (1)–(3)) was as follows:
  R 2 = [ i = 1 n O i O ¯ ( P i P ¯ ) ] 2 i = 1 n ( O i O ¯ ) ] 2 i = 1 n ( P i P ¯ ) ] 2
N S E = 1 i = 1 n ( O i P i ) 2 i = 1 n ( O i O ¯ ) 2
P B I A S = 100   ×   i = 1 n O i P i i = 1 n O i
where Oi represents the observed data, Pi represents the simulated data, O ¯ is the mean of the observed data, and P ¯ is the mean of the simulated data.

2.4. Principal Component Analysis of Land Use, Soil Type, and Slope Band on Water Balance

Principal Component Analysis (PCA) is one of the most widely used statistical techniques across many research fields [28]. It simplifies complex data by reducing its dimensionality and transforming multidimensional datasets into a smaller number of principal components. This makes it easier to analyze and reveal the relationships within the data. In this study, PCA was used to examine the relationships between land use, soil type, slope band, surface runoff, groundwater recharge, evapotranspiration, and water yield. Our objective was to identify and compare the potential correlations among these factors. The analysis was conducted using Origin Pro 2025 software.

2.5. Assessment of Water Supply, Demographic Trends, and Domestic Water Demand

Data on the water supply population and domestic water consumption were obtained from the Water Resources Planning Division, Water Resources Department, Water Management and Land Conservation Bureau, MLIT [29]. Specifically, we used data on the average water supply per person per day and the water supply population, which are available on the MLIT website. The retrieved data only covered 2007–2019, as these were the years available at the time of the study. Using this data, we computed the water supply population in relation to domestic water consumption (measured in millimeters) and groundwater availability (in millimeters) as part of the water balance calculation. Furthermore, we analyzed domestic water consumption as a percentage of the total groundwater flow, which allowed us to better understand the relationship between water usage and groundwater resources.

3. Results and Discussion

3.1. Model Performance and Uncertainty Analyses

During the calibration period (2007–2014), the model demonstrated strong performance, with a Nash-Sutcliffe Efficiency (NSE) of 0.81 and a coefficient of determination (R2) of 0.81, indicating high accuracy in simulating streamflow [Figure S3]. The Percent Bias (PBIAS) was −3.6%, suggesting a slight underestimation of discharge. This underestimation is within acceptable limits and reflects a consistent trend where the simulated cumulative discharge was lower than the observed discharge. In the validation period (2015–2021), the model maintained its reliability, with an NSE of 0.80 and R2 of 0.80 [Figure S4]. However, the fitting performance showed a slight decline, as indicated by an increased PBIAS of −5.7%. This further underestimation remains within acceptable thresholds, as the observed discharge exceeded the simulated discharge during both periods. The underestimation observed in both calibration and validation may be attributed to several factors. Notably, the model did not account for the contribution of reservoirs, as their influence was considered negligible for the scope of this study [30]. Additionally, semi-distributed models often face challenges related to input data quality, particularly in mountainous regions, where localized climatic variability can introduce errors in precipitation and temperature inputs. Despite these limitations, the model produced excellent calibration results, reflecting the quality of input data and the robustness of the model structure. As shown in Table 2, the negative PBIAS values confirm a consistent pattern of lower simulated discharge, which may result from unmodeled water losses, simplified land use representation, or limitations in capturing peak flow events. These insights reinforce the importance of refining model inputs and structure to improve simulation accuracy, especially under complex watershed conditions.
Figure 3 shows the relationship between the observed and simulated streamflows over the study period, providing insights into the model performance and hydrological dynamics in the Chikugo River basin. The simulated peak discharge exhibited significant variation when the model underestimated the peak flow during extreme flooding events. However, the observed and simulated discharges corresponded to the precipitation patterns of the basin. Overall, the SWAT model consistently produced high and moderate flows, particularly during rainy months. However, the streamflow was underestimated during the water stress months from October to November (following the monsoon season) and December to January. Asakura City experienced floods and severe heavy rainfall landslide disasters in 2017, owing to major extreme flow events in the summer of 2012 [31]. In 2020, another flood was considered to have the second-highest observed maximum daily peak discharge, reaching 6590 m3/s from 5 July to 7 July 2020, after the 1953 flood in the Chikugo catchment [32]. These extreme hydrological events not only pose severe risks to human settlements but may also disrupt downstream estuarine and marine ecosystems in the Ariake Sea. Sudden surges in freshwater inflow can alter salinity levels, sediment delivery, and nutrient dynamics, potentially affecting coastal biodiversity, primary productivity, and the ecological balance of the marine environment.
The observed and simulated temporal trends indicated the responsiveness of the basin to rainfall events, with sharp increases in streamflow following peak precipitation, reflecting the rapid runoff generation and transport characteristics of the basin [Figure S1]. The inaccuracy of peak discharge in the Chikugo catchment was possibly due to limited meteorological stations, the distance between stations, and uncertainty in GIS data for the spatial distribution of slope, land use, and soil [33]. Moreover, the ability of observed data to accurately record the peak discharge during extreme flooding periods may be limited, leading to uncertainty [34]. The major limitation was that rainfall intensity or duration was not accounted for. Runoff generated by short-duration, high-intensity storms may not all be largely simulated [35]. The agreement between the observed and simulated flows during low-flow periods indicated that the model captured baseflow contributions that are critical for maintaining streamflow during dry periods, thereby ensuring water availability for ecological and human needs [36].

3.2. Sensitive Parameter Analysis

Accurate parameterization is crucial in the calibration of a semi-distributed model characterized by many parameters and must be based on knowledge of the hydrological processes in the system [26]. The values of the 17 calibrated model parameters and their relative rank order of model performance based on the t-test and p-value are listed in Table 3. The model parameters CN2, CH_N2, and ALPHA_BF were more significant (p < 0.01) than other parameters at the catchment scale. Among these, the SCS runoff curve number (CN2) was among the most sensitive parameters for the Chikugo catchment. The next sensitive parameter was CH_N2, which represents Manning’s “n” value for the main channel and is used to describe the channel’s roughness, which affects water flow velocity. This parameter is important in hydrological modeling because it influences the rate at which water flows through the channel network [37]. Parameters such as the baseflow recession coefficient (ALPHA_BF) are important in hydrological modeling, especially in temperate climate catchments such as the Chikugo River basin. Its sensitivity originates from its role in regulating the groundwater discharge into streams, which are affected by seasonal precipitation patterns and subsurface characteristics. Variations in ALPHA_BF significantly impact the simulation of baseflow and overall water balance in temperate regions, affecting streamflow predictions during the dry and wet seasons [38,39,40]. Accurate parameter calibration was critical for accurate hydrological modeling in the Chikugo River basin, where seasonal rainfall and groundwater interactions play significant roles in hydrology.

3.3. Analysis of Annual Water Balance Components

Figure 4 shows the annual temporal variability across the water balance components of the Chikugo River basin from 2007 to 2021, indicating the influence of climatic fluctuations and anthropogenic activities. Surface runoff varied significantly between years, ranging from approximately 236 mm in 2012 to 123 mm in 2013, highlighting its sensitivity to precipitation intensity [41]. This fluctuation reflects the basin’s responsiveness to precipitation intensity and distribution, with high runoff years often coinciding with intense rainfall events or reduced infiltration capacities due to land use changes. However, baseflow varied significantly over time, serving as an important component in maintaining streamflow during dry periods while also reflecting fluctuations in groundwater contributions and recharge patterns. Recharge to deep aquifers remained relatively stable over time, indicating a less immediate impact of short-term climatic fluctuations on deep groundwater storage, as in most Japanese catchments [42,43]. Recharge to deep aquifers varied only slightly in all years, indicating that long-term groundwater reserves remained stable despite interannual climatic changes.
Evapotranspiration (ET), which consistently accounted for the largest proportion of the water balance, highlighted the dominant role of vegetation and climatic factors in driving hydrological processes in the basin [44,45,46]. The dominance of ET, which consistently exceeded 43% of precipitation, demonstrated the basin’s semi-humid climate and the significant influence of vegetation and atmospheric moisture demand [47]. This high ET directly impacted water resource availability, limiting the proportion of accessible water for surface runoff and groundwater recharge, and necessitating efficient water management practices [48], particularly in the lowland areas, which are flatter and more developed agricultural land (27%) and urban zones (10.41%), playing a growing role in shaping water movement. Precipitation variability was critical in shaping the interannual trends of all components, with wetter years, such as 2012, 2016, and 2021, showing elevated values across ET, baseflow, and runoff. However, drier years, such as 2007, 2008, and 2017, exhibited suppressed values (Figure 4), particularly in surface runoff and baseflow patterns. The combined trends of baseflow and recharge to deep aquifers revealed the dynamics of groundwater contributions and storage, with higher baseflow years, such as 2012, 2016, 2020, and 2021 (847–913.8 mm), exhibiting increased groundwater contributions following significant recharge periods [49,50]. These hydrological shifts are closely tied to the catchment’s physical characteristics. Forests dominate the landscape, covering nearly 58% of the area, especially in the steep upland and midstream zones where slopes exceed 30% in about a third of the catchment. Forests typically promote infiltration and reduce runoff, but under heavy or prolonged rainfall conditions that are becoming more common, soils can become saturated, leading to increased runoff and baseflow. The catchment’s soils, including Andosols, Cambisols, Acrisols, and Gleysols, have diverse water retention and drainage properties. For example, Andosols and Acrisols hold moisture well but may slow drainage, contributing to higher baseflow and recharge under wet conditions. This relationship is crucial for understanding the sustainability of groundwater use, especially under scenarios of increased water demand or reduced rainfall [51].

3.4. Analysis of Seasonal Variation of Water Balance Components

The seasonal variation in water balance components for the Chikugo River basin (Figure 5) reveals distinct hydrological behavior across winter, spring, summer, and autumn, shaped by the interaction of climatic drivers and watershed characteristics. These seasonal dynamics are critical for understanding temporal water availability and guiding adaptive water resource management. Evapotranspiration (ET) dominated the water balance in all seasons, reflecting its central role in the basin’s hydrological cycle. The highest ET was observed in summer (354.9 mm), followed by spring (231.3 mm), autumn (202.7 mm), and winter (101.2 mm), consistent with seasonal variations in temperature and solar radiation [52,53,54]. This pattern highlights the strong influence of atmospheric demand and vegetation activity, particularly during the growing season. The elevated summer ET also indicates increased water loss from both soil and vegetation, which can reduce water availability for runoff and recharge, especially in agricultural zones where irrigation demand is high. Baseflow peaked in summer (379.1 mm), followed by autumn (186 mm), spring (179.7 mm), and winter (126.5 mm). This seasonal trend suggests that groundwater discharge into the river system is enhanced during warmer months, likely due to delayed recharge from spring precipitation and sustained soil moisture. The high summer baseflow also reflects the basin’s subsurface connectivity and the role of shallow aquifers in maintaining streamflow during periods of low rainfall. These dynamics are particularly important for sustaining ecological flows and water supply during dry seasons. In Kyushu Island, Japan, the planting season largely depends on the crop type and the region’s warm temperate climate. During the rice growing seasons (spring and summer), maintaining baseflow is important, especially outside the rainy season, as it ensures stable streamflow, which supports irrigation and buffers the effects of climate extremes, especially when surface runoff is unpredictable or ET is high. Therefore, effective water management allocation during this transition is essential to balance domestic and agricultural water demand, flood control, and groundwater recharge. Recharge to deep aquifers remained relatively stable across seasons, ranging from 40.8 mm in winter to 51.8 mm in autumn. This consistency indicates that deep groundwater systems are buffered against short-term climatic fluctuations, a characteristic typical of Japanese catchments with well-developed aquifer systems [55,56]. The slight seasonal variation may be attributed to differences in infiltration rates and soil moisture retention, but overall, the recharge stability supports long-term groundwater sustainability in the basin. Surface runoff (SURFQ) showed the most pronounced seasonal variation, with the highest value in summer (103.7 mm), followed by spring (37.3 mm), autumn (33.4 mm), and winter (23.9 mm). The substantial surface runoff during summer underscores the need for effective flood management strategies to mitigate the potential risks associated with intense rainfall and reduced infiltration. This reduced infiltration can result from high evapotranspiration rates, vegetation water uptake, and surface compaction caused by intense precipitation, all of which restrict water percolation despite increased rainfall [57,58]. By linking climatic variability to water balance components, the analysis supports the broader research objective of enhancing watershed resilience and informing seasonally adaptive water resource planning under changing climate conditions.

3.5. Relationship Between Land Use, Soil Type, and Slope Band on Water Balance

The PCA biplot in Figure 6 offers a clear visualization of the relationships among various environmental variables in the Chikugo catchment, revealing key insights into how soil types, land use, slope, and climatic factors interact and influence catchment characteristics. The first principal component (PC1), accounting for 29.54% of the variance, aligns closely with ETmm (evapotranspiration), GWQmm (groundwater quantity), and, to a lesser extent, soil type and land use, suggesting these variables collectively represent a gradient of hydrological and land surface processes. The second component (PC2), explaining 15.89% of the variance, aligns primarily with soil type and land use, and slightly with slope band, indicating a secondary gradient that differentiates catchment areas based on terrain and soil characteristics. In the context of the Chikugo catchment, where Andosols, Cambisols, Acrisols, and Gleysols are dominant soils, the clustering of soil type with land use along PC2 reflects the strong spatial heterogeneity of the catchment. The upper and midstream areas, dominated by forest cover (57.47%) on steeper slopes (with 34% of the catchment having slopes greater than 30%, likely correspond to soils such as Andosols and Cambisols, which favor forest growth and influence evapotranspiration and groundwater recharge dynamics. These forested areas show a clear association with higher PC2 scores, indicating distinct soil and land use patterns that impact hydrological processes. In contrast, the lowland areas are characterized by agricultural land (27%), and urban regions (10.41%) cluster on the opposite end of PC1, indicating a different hydrological regime likely influenced by soil types such as Gleysols and Acrisols that are more common in floodplains and low slopes (0–10%). This spatial distinction is critical, as agricultural and urban land uses generally reduce infiltration and increase surface runoff, thus impacting groundwater recharge and evapotranspiration patterns differently than forested regions.
The negative values of the slope band on PC2 suggest that slope influences soil and land use relationships, with steeper areas supporting forest soil and land use, while flatter areas support agricultural and urban land uses. This relationship is supported by a study conducted by Nang et al. [59] in the Takashi Catchment, which found that slope gradient has the most pronounced effect on bareland, agricultural, and rice land covers, resulting in 2.2 t ha−1 year−1 at slope > 45%. Climatic variables such as SURQmm (surface runoff) and WYLDmm (water yield) have negative values on PC1, indicating an inverse relationship with ETmm and groundwater quantities, which aligns with the catchment’s hydrological behavior, where higher runoff often corresponds with lower groundwater recharge. This relationship is further explained by previous studies by Guyo et al. [60] and Wetherl et al. [61], which highlights the interaction between ground and surface water and the role of vegetation and land cover in regulating watershed hydrology. Moreover, rapid urbanization and the impact of vegetation degradation on groundwater levels have led to changes in water balance due to increased impervious surfaces that enhance flooding and over extraction of groundwater [62,63]. Urban development accelerates runoff and limits infiltration, while agricultural practices can either support or hinder infiltration depending on soil management. According to Gatwaza et al. [64], surface runoff is the most sensitive component affected by land use changes. Other studies by Kimbi et al. [65] and Kibii et al. [66] have reported the combined effects of climate and land use changes, especially more intense and irregular rainfall, intensifying hydrological variability and pressure on water resources.

3.6. Domestic Water Use for Sustainable Water Resource Management

Domestic water use is a critical component of the overall water balance in the Chikugo River Basin, directly linking hydrological processes to human demand and long-term sustainability. The Chikugo River provides water essential for both domestic and agricultural use, benefiting the lives of many people. Domestic water intake from the river is distributed across three main areas: the Eastern Saga Water Supply Authority, the Greater Southern Fukuoka Water Supply Authority, and Fukuoka District Water Supply (Fukuoka Canal), also extending to other regions. According to data from MLIT, over 3.6 million people rely on the Chikugo River for their water supply. This represents approximately 83.3% of the population of Fukuoka Prefecture, 14.2% of the population of Saga Prefecture, 1% of the population of Kumamoto, and 1.6% of the population of Oita Prefecture. Domestic water use remained relatively stable, averaging 142.4 mm/year over the study period. The highest recorded consumption was 147.8 mm/year in 2011, whereas the lowest was 137 mm/year in 2014, exhibiting a 7.4% variation. The temporary decline during 2012–2014 may be attributed to improved water efficiency measures or climate-related factors affecting demand.
Groundwater availability showed significant interannual variability, fluctuating between 629.9 mm/year (2007) and 872.1 mm/year (2016), reflecting a 38.4% difference (Table 4). The highest groundwater availability in 2016 (872.1 mm/year) coincided with an increase in domestic water use (142.0 mm/year), suggesting a potential correlation with recharge conditions and extraction levels. However, after 2016, groundwater availability declined by 14.8% in just 1 year (to 743.2 mm/year in 2017), remaining below 800 mm/year for the subsequent years. The increasing population in the Chikugo catchment, especially around rapidly urbanizing areas like Fukuoka City, has led to a gradual rise in water consumption. In this study, while groundwater availability has remained stable due to sufficient precipitation, the combined pressure from domestic, agricultural, and industrial demand underscores the urgency of integrating water management strategies that balance urban growth with sustainable groundwater use into hydrological assessments and planning frameworks. According to studies conducted in urban regions of Japan, such as Tokyo, Osaka, Chiba, and Toyama, improved water use efficiency and policy interventions have played a significant role in addressing the fragility of Japan’s water resource systems. These efforts highlight the importance of adopting more coordinated and flexible water management strategies to achieve sustainable outcomes [67,68,69].

4. Conclusions

The model demonstrated strong performance with Nash–Sutcliffe Efficiency (NSE) and coefficient of determination (R2) of 0.81 (calibration and validation) with less PBIAS, confirming its reliability for hydrological assessments in complex and data-scarce catchments. It also highlights the importance of baseflow, especially during the spring and early summer season, in maintaining the hydrological stability in the Chikugo catchment, particularly in meeting agricultural and domestic water demands across varying seasons. Moreover, the Principal Component analysis (PCA) reveals that evapotranspiration (ET), groundwater (GW), and land use are the most influential factors contributing to hydrological variability, with PC1 and PC2 explaining a combined 45.43% of the total variance. Additionally, domestic water use accounted for 16.3% to 22.8% of total groundwater flow, with notable temporal fluctuations, emphasizing the need for continuous monitoring, responsive water allocation, and adaptive water management. Overall, the findings contribute to advancing hydrological modeling in Kyushu regions, support climate-resilient water governance, and provide a scientific foundation for addressing transboundary water security challenges. Addressing existing data limitations and exploring future scenarios further will enhance the model’s reliability and support sustainable water resource management.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17157027/s1, Table S1. Meteorological station distribution and its average annual precipitation (2007–2021); Table S2. Annual Mean Observed and Simulated Precipitation in the Chikugo Catchment; Figure S1. Comparison of Annual Mean Observed and Simulated Precipitation in the Chikugo Catchment; Table S3. Correspondence between MLIT and SWAT land use code classification; Table S4. Distribution of Soil Types by Area and Percentage in the Watershed; Table S5. Land use classes coverage and comparison of land use change in 2006 and 2021; Figure S2. Flow chart of water balance estimation methodology; Figure S3. Scatter plots for stream flow calibration (2007–2014); Figure S4. Scatter plots for stream flow validation (2015–2021).

Author Contributions

Conceptualization: S.-i.O., M.S., and K.W.; methodology: K.W. and F.J.M.; validation: F.J.M.; investigation: Y.N. and M.Y.; resources: S.-i.O., M.S., Y.N., and M.Y.; writing—original draft: F.J.M.; writing—review and editing: K.W., Y.W.N., S.-i.O., and M.S.; supervision: S.-i.O., K.W., M.S., and Y.W.N.; project administration: S.-i.O., Y.N., and M.Y.; funding acquisition: S.-i.O. and Y.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research recieved no funding from the co-authors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

This manuscript is based on data generated during the research of Francis Jhun Macalam, supported by the Philippine Department of Science and Technology—Foreign Graduate Scholarship Program and his home institution, the University of Science and Technology of Southern Philippines. This study was conducted with the assistance of data provided by the Project of Mountain Conservation Research, in Ariake Sea Catchments, supported by the Forestry Agency, Japan.

Conflicts of Interest

Author Masatoshi Yamazaki was employed by the company PASCO. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. van Vliet, M.T.H.; Jones, E.R.; Flörke, M.; Franssen, W.H.P.; Hanasaki, N.; Wada, Y.; Yearsley, J.R. Global Water Scarcity Including Surface Water Quality and Expansions of Clean Water Technologies. Environ. Res. Lett. 2021, 16, 024020. [Google Scholar] [CrossRef]
  2. Han, Z.Y.; Han, P.F.; Huang, Q.; Du, M.D.; Hou, A.Z. An Improved Modeling of Precipitation Phase and Snow in the Lancang River Basin in Southwest China. Sci. China Technol. Sci. 2021, 64, 1513–1527. [Google Scholar] [CrossRef]
  3. Kamamia, A.W.; Vogel, C.; Mwangi, H.M.; Feger, K.H.; Sang, J.; Julich, S. Using Soil Erosion as an Indicator for Integrated Water Resources Management: A Case Study of Ruiru Drinking Water Reservoir, Kenya. Environ. Earth Sci. 2022, 81, 502. [Google Scholar] [CrossRef]
  4. Safeeq, M.; Bart, R.R.; Pelak, N.F.; Singh, C.K.; Dralle, D.N.; Hartsough, P.; Wagenbrenner, J.W. How Realistic Are Water-Balance Closure Assumptions? A Demonstration From the Southern Sierra Critical Zone Observatory and King’s River Experimental Watersheds. Hydrol. Process. 2021, 35, e14199. [Google Scholar] [CrossRef]
  5. Gupta, L.; Dixit, J. Estimation of Rainfall-Induced Surface Runoff for the Assam Region, India, Using the GIS-Based NRCS-CN Method. J. Maps 2022, 18, 428–440. [Google Scholar] [CrossRef]
  6. Ding, T.; Gao, H. The Record-Breaking Extreme Drought in Yunnan Province, Southwest China During Spring-Early Summer of 2019 and Possible Causes. J. Meteorol. Res. 2020, 34, 997–1012. [Google Scholar] [CrossRef]
  7. Sinclair, S.; Pegram, G.G.S. A Comparison of ASCAT and Modelled Soil Moisture Over South Africa, Using TOPKAPI in Land Surface Mode. Hydrol. Earth Syst. Sci. 2010, 14, 613–626. [Google Scholar] [CrossRef]
  8. Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L. Soil and Water Assessment Tool. In Theoretical Documentation Version 2009; Technical Report No. 406; Texas Water Resources Institute, Texas A&M University: College Station, TX, USA, 2011; Available online: https://www.researchgate.net/publication/306205252 (accessed on 5 January 2025).
  9. Halwatura, D.; Najim, M.M.M. Application of the HEC-HMS Model for Runoff Simulation in a Tropical Catchment. Environ. Model. Softw. 2013, 46, 155–162. [Google Scholar] [CrossRef]
  10. Tan, M.L.; Gassman, P.W.; Yang, X.; Haywood, J. A Review of SWAT Applications, Performance and Future Needs for Simulation of Hydro-Climatic Extremes. Adv. Water Resour. 2020, 143, 103662. [Google Scholar] [CrossRef]
  11. Wang, K.; Onodera, S.I.; Saito, M. Evaluation of Nitrogen Loading in the Last 80 Years in an Urbanized Asian Coastal Catchment Through the Reconstruction of Severe Contamination Period. Environ. Res. Lett. 2022, 17, 014010. [Google Scholar] [CrossRef]
  12. Wang, K.; Onodera, S.I.; Saito, M.; Ishida, T. Assessment of Long-Term Phosphorus Budget Changes Influenced by Anthropogenic Factors in a Coastal Catchment of Osaka Bay. Sci. Total Environ. 2022, 843, 156833. [Google Scholar] [CrossRef]
  13. Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil & Water Assessment Tool: Theoretical Documentation Version 2009; Texas Water Resources Institute: College Station, TX, USA, 2011; Available online: https://swat.tamu.edu/media/99192/swat2009-theory.pdf (accessed on 8 January 2025).
  14. Chinh, L.V.; Iseri, H.; Hiramatsu, K.; Harada, M.; Mori, M. Simulation of Rainfall Runoff and Pollutant Load for Chikugo River Basin in Japan Using a GIS-Based Distributed Parameter Model. Paddy Water Environ. 2013, 11, 97–112. [Google Scholar] [CrossRef]
  15. Dile, Y.T.; Daggupati, P.; George, C.; Srinivasan, R.; Arnold, J. Introducing a New Open Source GIS User Interface for the SWAT Model. Environ. Model. Softw. 2016, 85, 129–138. [Google Scholar] [CrossRef]
  16. Azhikodan, G.; Yokoyama, K. Seasonal Morphodynamic Evolution in a Meandering Channel of Macrotidal Estuary. Sci. Total Environ. 2019, 684, 281–295. [Google Scholar] [CrossRef]
  17. Suzuki, K.W.; Nakayama, K.; Tanaka, M. Horizontal Distribution and Population Dynamics of the Dominant Mysid Hyperacanthomysis longirostris Along a Temperate Macrotidal Estuary (Chikugo River Estuary, Japan). Estuar. Coast. Shelf Sci. 2009, 83, 516–528. [Google Scholar] [CrossRef]
  18. Saquib, S.; Gupta, A.; Joshi, A. Emerging Water Crisis: Impact of Urbanization on Water Resources and Constructed Wetlands as a Nature-Based Solution (NbS). Curr. Dir. Water Scarcity Res. 2022, 6, 447–468. [Google Scholar] [CrossRef]
  19. U.S. Geological Survey. Earth Explorer; Department of the Interior: Reston, VA, USA, 2023. Available online: https://earthexplorer.usgs.gov/ (accessed on 20 October 2023).
  20. Ministry of Land, Infrastructure, Transport and Tourism. Land Classif. Surv. Data [Shapefile]; Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2023. Available online: https://nlftp.mlit.go.jp/kokjo/inspect/landclassification/download.html (accessed on 24 November 2023).
  21. Ministry of Land, Infrastructure, Transport and Tourism. Land Use Subdivision Mesh Data (L03-b); Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2023. Available online: https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-L03-b-v3_1.html (accessed on 24 November 2023).
  22. Japan Meteorological Agency. Weather Map and AMeDAS Observation Data; Japan Meteorological Agency: Tokyo, Japan, 2023. Available online: https://www.jma.go.jp/jma/index.html (accessed on 24 November 2023).
  23. Ministry of Land, Infrastructure, Transport and Tourism. Water Level Obs. Site Inf. [CSV/Excel]; Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2023. Available online: http://www1.river.go.jp/cgi-bin/SiteInfo.exe?ID=309061289901190 (accessed on 24 November 2023).
  24. Abbaspour, K.C.; Vejdani, M.; Haghighat, S.; Yang, J. SWAT-CUP Calibration and Uncertainty Programs for SWAT. In Proceedings of the MODSIM 2007 International Congress on Modelling and Simulation, Christchurch, New Zealand, 10–13 December 2007; pp. 1596–1602. Available online: https://scholar.google.com/scholar?cluster=7621557009645148872 (accessed on 5 September 2024).
  25. Hargreaves, G.H.; Samani, Z.A. Estimating Potential Evapotranspiration. J. Irrig. Drain. Eng. 1982, 108, 223–230. [Google Scholar] [CrossRef]
  26. Arnold, J.G.; Moriasi, D.N.; Gassman, P.W.; Abbaspour, K.C.; White, M.J.; Srinivasan, R.; Santhi, C.; Harmel, D.; van Griensven, A.; Van Liew, M.W.; et al. SWAT: Model Use Calibration Validation. Trans. ASABE 2012, 55, 1491–1508. [Google Scholar] [CrossRef]
  27. Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, D.R.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 2007, 50, 885–900. Available online: https://swat.tamu.edu/media/1312/moriasimodeleval.pdf (accessed on 15 May 2024). [CrossRef]
  28. Abdi, H.; Williams, L.J. Principal Component Analysis. Wiley Interdiscip. Rev. Comput. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
  29. Ministry of Land, Infrastructure, Transport and Tourism. Water Resour. Data Summ. [Excel File]; Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2023. Available online: https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fwww.mlit.go.jp%2Fmizukokudo%2Fmizsei%2Fcontent%2F001593175.xlsx (accessed on 2 February 2025).
  30. Wang, K.; Onodera, S.I.; Saito, M.; Okuda, N.; Okubo, T. Estimation of Phosphorus Transport Influenced by Climate Change in a Rice Paddy Catchment Using SWAT. Int. J. Environ. Res. 2021, 15, 759–772. [Google Scholar] [CrossRef]
  31. Japan Society of Civil Engineers. Heisei 29 July Kyushu Northern Heavy Rain Disaster Investigation Report; Japan Society of Civil Engineers: Tokyo, Japan, 2020; Available online: https://committees.jsce.or.jp/report/node/209 (accessed on 18 March 2025).
  32. Phyu, P.E.; Azhikodan, G.; Yokoyama, K. Effects of Past Human Activities and Recent Disasters on Riverbed Morphology of the Chikugo River Estuary. J. JSCE 2024, 12, 23–16019. [Google Scholar] [CrossRef]
  33. Kibet, K.B.; Takeuchi, J.; Fujihara, M. Applicability of SWAT Model for Streamflow Simulation in a Highly Managed Agricultural Watershed. J. Rainwater Catchment Syst. 2018, 23, 19–29. Available online: https://www.jstage.jst.go.jp/article/jrcsa/23/2/23_23_2_19/_pdf/-char/ja (accessed on 25 March 2025). [CrossRef] [PubMed]
  34. Wang, K.; Onodera, S.I.; Saito, M.; Shimizu, Y. Assessment of Nitrogen Budget in Detailed Spatial Pattern Using High Precision Modeling Approach With Constructed Accurate Agricultural Behavior. Sci. Total Environ. 2024, 912, 169631. [Google Scholar] [CrossRef]
  35. King, K.W.; Arnold, J.G.; Bingner, R.L. Comparison of Green-Ampt and Curve Number Methods on Goodwin Creek Watershed Using SWAT. Trans. ASAE 1999, 42, 919–925. [Google Scholar] [CrossRef]
  36. Schlef, K.E.; François, B.; Brown, C. Comparing Flood Projection Approaches Across Hydro-Climatologically Diverse United States River Basins. Water Resour. Res. 2021, 57, e2019WR025861. [Google Scholar] [CrossRef]
  37. Boithias, L.; Sauvage, S.; Lenica, A.; Roux, H.; Abbaspour, K.; Larnier, K.; Dartus, D.; Perez, J.M.S. Simulating Flash Floods at Hourly Time-Step Using the SWAT Model. Water 2017, 9, 929. [Google Scholar] [CrossRef]
  38. Lee, J.; Kim, S.; Park, J.; Arnold, J.G. Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT. Sustainability 2024, 16, 9189. [Google Scholar] [CrossRef]
  39. Sharma, R.; Singh, S. Streamflow and Sediment Simulation in the Song River Basin Using SWAT. Front. Water 2025, 3, 1500086. [Google Scholar] [CrossRef]
  40. Aboelnour, M.; Gitau, M.W.; Engel, B.A. A Comparison of Streamflow and Baseflow Responses to Land-Use Change and the Variation in Climate Parameters Using SWAT. Water 2020, 12, 191. [Google Scholar] [CrossRef]
  41. Kubota, S.; Nishida, K.; Yoshida, S. Plant Hydraulic Resistance Controls Transpiration of Soybean in Rotational Paddy Fields Under Humid Climates. Paddy Water Environ. 2023, 21, 219–230. [Google Scholar] [CrossRef]
  42. Lu, Z.; Feng, Q.; Xie, J.; Yin, Z.; Zhu, M.; Xu, M. The Reallocation of Water and Ecosystem Service Values in Arid Ecosystems With the Implementation of an Ecological Water Diversion Project. Appl. Water Sci. 2023, 13, 93. [Google Scholar] [CrossRef]
  43. Wu, D.; Fang, S.; Tong, X.; Wang, L.; Zhuo, W.; Pei, Z.; Wu, Y.; Zhang, J.; Li, M. Analysis of Variation in Reference Evapotranspiration and Its Driving Factors in Mainland China From 1960 to 2016. Environ. Res. Lett. 2021, 16, 054016. [Google Scholar] [CrossRef]
  44. Lo, M.-H.; Wey, H.-W.; Im, E.-S.; Tang, L.I.; Anderson, R.G.; Wu, R.-J.; Chien, R.-Y.; Wei, J.f.; AghaKouchak, A.; Wada, Y. Intense Agricultural Irrigation Induced Contrasting Precipitation Changes in Saudi Arabia. Environ. Res. Lett. 2021, 16, 064049. [Google Scholar] [CrossRef]
  45. Scanlon, B.R.; Rateb, A.; Pool, D.R.; Sanford, W.; Save, H.; Sun, A.; Long, D.; Fuchs, B. Effects of Climate and Irrigation on GRACE-Based Estimates of Water Storage Changes in Major US Aquifers. Environ. Res. Lett. 2021, 16, 094009. [Google Scholar] [CrossRef]
  46. Xiong, J.; Abhishek, Y.; Xu, L.; Chandanpurkar, H.A.; Famiglietti, J.S.; Zhang, C.; Ghiggi, G.; Guo, S.; Pan, Y.; Vishwakarma, B.D. ET-WB: Water Balance-Based Estimations of Terrestrial Evaporation Over Global Land and Major Global Basins. arXiv 2023, arXiv:2305.08881. [Google Scholar] [CrossRef]
  47. Yi, W.; Feng, Y.; Liang, S.; Kuang, X.; Yan, D.; Wan, L. Increasing Annual Streamflow and Groundwater Storage in Response to Climate Warming in the Yangtze River Source Region. Environ. Res. Lett. 2021, 16, 084011. [Google Scholar] [CrossRef]
  48. Wang, K.; Onodera, S.I.; Saito, M.; Shimizu, Y.; Iwata, T. Effects of Forest Growth in Different Vegetation Communities on Forest Catchment Water Balance. Sci. Total Environ. 2022, 809, 151159. [Google Scholar] [CrossRef] [PubMed]
  49. Kebede, S.; Charles, K.; Godfrey, S.; MacDonald, A.; Taylor, R. Regional-Scale Interactions Between Groundwater and Surface Water Under Changing Aridity: Evidence From the River Awash Basin, Ethiopia. Hydrol. Sci. J. 2021, 66, 450–463. [Google Scholar] [CrossRef]
  50. Xu, L.; Sun, S.; Chen, H.; Chai, H.; Wang, R.; Zhou, Y.; Ma, Q.; Chotamonsak, C.; Wangpakapattanawong, P. Changes in the Reference Evapotranspiration and Contributions of Climate Factors Over the Indo–China Peninsula During 1961–2017. Int. J. Climatol. 2021, 41, 6511–6529. [Google Scholar] [CrossRef]
  51. KanthaRao, B.; Rakesh, V. Observational Evidence for the Relationship Between Spring Soil Moisture and June Rainfall Over the Indian Region. Theor. Appl. Climatol. 2017, 132, 835–849. [Google Scholar] [CrossRef]
  52. Matheswaran, K.; Khadka, A.; Dhaubanjar, S.; Bharati, L.; Kumar, S.; Shrestha, S. Delineation of Spring Recharge Zones Using Environmental Isotopes to Support Climate-Resilient Interventions in Two Mountainous Catchments in Far-Western Nepal. Hydrogeol. J. 2019, 27, 2181–2197. [Google Scholar] [CrossRef]
  53. Rezaei, A. Chemistry of the Karst Sarabkalan Spring, Iran, and Controls of PDO and ENSO Climate Indices on It. Groundwater 2020, 59, 236–244. [Google Scholar] [CrossRef] [PubMed]
  54. Ferede, M.; Haile, A.T.; Walker, D.; Gowing, J.; Parkin, G. Multi-Method Groundwater Recharge Estimation at Eshito Micro-Watershed, Rift Valley Basin in Ethiopia. Hydrol. Sci. J. 2020, 65, 1596–1605. [Google Scholar] [CrossRef]
  55. Harrison, H.; Hammond, J.C.; Kampf, S.; Kiewiet, L. On the Hydrological Difference Between Catchments Above and Below the Intermittent-Persistent Snow Transition. Hydrol. Process. 2021, 35, e14411. [Google Scholar] [CrossRef]
  56. Wang, K.; Onodera, S.I.; Saito, M.; Shimizu, Y. Long-Term Variations in Water Balance by Increase in Percent Imperviousness of Urban Regions. J. Hydrol. 2021, 602, 126767. [Google Scholar] [CrossRef]
  57. Trancoso, R.; Larsen, J.R.; McVicar, T.R.; Phinn, S.R.; McAlpine, C.A. CO2-Vegetation Feedback and Other Climate Changes Implicated in Reducing Base Flow. Geophys. Res. Lett. 2017, 44, 2310–2318. [Google Scholar] [CrossRef]
  58. Sabathier, R.; Ainger, M.B.; Stella, J.C.; Roberts, D.A.; Caylor, K.K.; Jaeger, K.L.; Olden, J.D. High Resolution Spatiotemporal Patterns of Flow at the Landscape Scale in Montane Non-Perennial Streams. River Res. Appl. 2022, 39, 225–240. [Google Scholar] [CrossRef]
  59. Nang, Y.W.; Onodera, S.I.; Wang, K.; Shimizu, Y.; Saito, M. Slope Gradient Effects on Sediment Yield of Different Land Cover and Soil Types. Water 2024, 16, 1419. [Google Scholar] [CrossRef]
  60. Guyo, R.H.; Wang, K.; Saito, M.; Onodera, S.; Shimizu, Y.; Moroizumi, T. Spatiotemporal Shallow and Deep Groundwater Dynamics in a Forested Mountain Catchment with Diverse Slope Gradients, Western Japan. Groundw. Sustain. Dev. 2024, 25, 101150. [Google Scholar] [CrossRef]
  61. Weatherl, R.K.; Helanao Salgado, M.J.; Ramgraber, M.; Moeck, C.; Schirmer, M. Estimating surface runoff and groundwater recharge in an urban catchment using a water balance approach. Hydrogeol. J. 2021, 29, 2411–2428. [Google Scholar] [CrossRef]
  62. Emre, B.; Baba, A. Effect of Urbanization on Water Resources: Challenges and Prospects. In Groundwater in Arid and Semi-Arid Areas; Ali, S., Armanuos, A.M., Eds.; Springer: Cham, Switzerland, 2023. [Google Scholar] [CrossRef]
  63. Sajjad, M.M.; Wang, J.; Abbas, H.; Ullah, I.; Khan, R.; Ali, F. Impact of climate and land use change on groundwater resources: Study of Faisalabad district, Pakistan. Atmosphere 2022, 13, 1097. [Google Scholar] [CrossRef]
  64. Gatwaza, O.C.; Cao, X.; Becline, M. Impact of Urbanization on the hydrological cycle of Migina Catchment, Rwanda. Open Access Libr. J. 2006, 3, 1–12. [Google Scholar] [CrossRef]
  65. Kimbi, S.B.; Onodera, S.-I.; Wang, K.; Kaihotsu, I.; Shimizu, Y. Assessing the Impact of Urbanization and Climate Change on Hydrological Processes in a Suburban Catchment. Environments 2024, 11, 225. [Google Scholar] [CrossRef]
  66. Kibii, J.K.; Kipkorir, E.C.; Kosgei, J.R. Application of soil and water assessment tool (SWAT) to evaluate the impact of land use and climate variability on the Kaptagat catchment river discharge. Sustainability 2021, 13, 1802. [Google Scholar] [CrossRef]
  67. Hong, C.-Y.; Tanaka, K. Exploring Urban Flood Policy Trends Using a Socio-Hydrological Approach—Case Studies from Japanese Cities. Sustainability 2023, 15, 13587. [Google Scholar] [CrossRef]
  68. Vicente-Serrano, S.M.; Peña-Angulo, D.; Beguería, S.; Domínguez-Castro, F.; Tomás-Burguera, M.; Noguera, I.; Gimeno-Sotelo, L.; El Kenawy, A. Global drought trends and future projections. Philos. Trans. R. Soc. A 2022, 380, 20210285. [Google Scholar] [CrossRef]
  69. Savelli, E.; Rusca, M.; Cloke, H.; Di Baldassarre, G. Drought and society: Scientific progress, blind spots, and future prospects. Wiley Interdiscip. Rev. Clim. Change 2022, 13, e761. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (a) Distribution of meteorological and hydrological stations [Table S1] and location map of Chikugo catchment. (b) Soil type map, and (c) Land use map of Chikugo catchment extracted from the Ministry of Land Infrastructure, Transport, and Tourism (MLIT).
Figure 1. (a) Distribution of meteorological and hydrological stations [Table S1] and location map of Chikugo catchment. (b) Soil type map, and (c) Land use map of Chikugo catchment extracted from the Ministry of Land Infrastructure, Transport, and Tourism (MLIT).
Sustainability 17 07027 g001
Figure 2. Land use classification in the Chikugo catchment for the years 2006 (a) and 2021 (b). The maps show significant land use changes, emphasizing large decreases in agricultural paddy fields and evergreen forested areas with an increase in built-up areas.
Figure 2. Land use classification in the Chikugo catchment for the years 2006 (a) and 2021 (b). The maps show significant land use changes, emphasizing large decreases in agricultural paddy fields and evergreen forested areas with an increase in built-up areas.
Sustainability 17 07027 g002
Figure 3. Comparison between observed and simulated daily streamflow vs precipitation.
Figure 3. Comparison between observed and simulated daily streamflow vs precipitation.
Sustainability 17 07027 g003
Figure 4. Average annual water balance ratio between 2007 and 2021.
Figure 4. Average annual water balance ratio between 2007 and 2021.
Sustainability 17 07027 g004
Figure 5. Seasonal distribution of catchment hydrological components.
Figure 5. Seasonal distribution of catchment hydrological components.
Sustainability 17 07027 g005
Figure 6. Principal Component Analysis of land use, soil type, and slope band influences on hy-drological processes.
Figure 6. Principal Component Analysis of land use, soil type, and slope band influences on hy-drological processes.
Sustainability 17 07027 g006
Table 1. Spatial datasets and their sources.
Table 1. Spatial datasets and their sources.
Data SetFormatTemporal/
Spatial Resolution
Data Source
Digital Elevation Model (DEM)raster30 m[19]
Soil typeshapefile1:500,000[20]
Land use (2014)shapefile100 m (grid cells)[21]
Weather data.csvDaily (2004–2021)[22]
Discharge (m3/s).csvDaily (2004–2021)[23]
Table 2. Model performance statistics calibration and validation results.
Table 2. Model performance statistics calibration and validation results.
Model Performance Evaluation
and Uncertainty
Modeling
Period
R2NSEPBIAS
Calibration2007–20140.810.81−3.6
Validation2015–20210.800.80−5.7
Note: NSE, Nash Sutcliffe efficiency; R2, Regression; PBIAS, Percent of Bias.
Table 3. List of sensitive parameters, minimum and maximum values, and statistical values according to rank.
Table 3. List of sensitive parameters, minimum and maximum values, and statistical values according to rank.
ParameterFile Ext.MethodDescriptionMin_
Value
Max_
Value
p-ValueT-statRank
CN2.mgtrInitial SCS-CN moisture condition II−0.0030.0880.00090.561
CH_N2.hruvManning’s “n” value for the main channel0.0400.0780.000−5.792
ALPHA_BF.bsnvBaseflow alpha factor0.5000.6000.0092.683
LAT_TTIME.hruvLateral flow travel time1591620.022−2.334
CANMX.hruvMaximum canopy storage (mm)32350.026−2.265
CH_K2.hruvEffective hydraulic conductivity in the main channel alluvium28290.058−1.926
GWQMN.gwvThreshold depth of water in the shallow aquifer required for return flow to occur223022400.070−1.837
GW_DELAY.gwvGroundwater delay4404450.121−1.5678
OV_N.hrurManning’s “n” value for overland flow−0.20−0.0700.131−1.5269
ESCO.hruvSoil evaporation compensation factor0.850.950.27761.09610
SOL_K.solrSoil saturated hydraulic conductivity −0.0570.1290.288−1.07011
HRU_SLP.hrurAverage slope steepness−0.560−0.5700.335−0.97112
GW_REVAP.gwvGroundwater “revap” coefficient 9.19.30.4180.81313
SLSUBBSN.hrurAverage slope length0.4400.4510.528−0.63414
SOL_AWC.solrAvailable water capacity of the soil layer−0.609−0.1350.573−0.56515
REVAPMN.gwvThreshold depth of water in the shallow aquifer for “revap” to occur7517520.736−0.33816
SOL_BD.solrMoist bulk density0.3120.3130.998−8.84317
Note: r indicates that the parameter value is changed relatively, and v indicates that the default parameter is replaced.
Table 4. Trends in Water Supply Population, Domestic Water use, and Groundwater Availability in the Chikugo catchment.
Table 4. Trends in Water Supply Population, Domestic Water use, and Groundwater Availability in the Chikugo catchment.
YearPrecipitation (mm)Water Supply Population
(million)
Domestic Water Consumption (mm)Groundwater Availability (mm)Domestic water
Consumption As a
Proportion of Total Groundwater Flow (%)
20071760.33.52143.6629.922.8
20081783.23.54145.4731.419.9
20091914.13.57144.4774.718.6
20101856.13.58146.8769.119.1
20112094.13.61147.8746.119.8
20122285.23.69138.0847.016.3
20132085.13.70139.0754.318.4
20141927.73.73137.0754.718.1
20152156.33.75139.7809.217.3
20162509.33.77142.0872.116.3
20171704.63.79145.0743.119.5
20182058.53.80144.8800.418.1
20191905.93.82145.0790.218.3
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Macalam, F.J.; Wang, K.; Onodera, S.-i.; Saito, M.; Nagano, Y.; Yamazaki, M.; Nang, Y.W. Hydrological Modeling of the Chikugo River Basin Using SWAT: Insights into Water Balance and Seasonal Variability. Sustainability 2025, 17, 7027. https://doi.org/10.3390/su17157027

AMA Style

Macalam FJ, Wang K, Onodera S-i, Saito M, Nagano Y, Yamazaki M, Nang YW. Hydrological Modeling of the Chikugo River Basin Using SWAT: Insights into Water Balance and Seasonal Variability. Sustainability. 2025; 17(15):7027. https://doi.org/10.3390/su17157027

Chicago/Turabian Style

Macalam, Francis Jhun, Kunyang Wang, Shin-ichi Onodera, Mitsuyo Saito, Yuko Nagano, Masatoshi Yamazaki, and Yu War Nang. 2025. "Hydrological Modeling of the Chikugo River Basin Using SWAT: Insights into Water Balance and Seasonal Variability" Sustainability 17, no. 15: 7027. https://doi.org/10.3390/su17157027

APA Style

Macalam, F. J., Wang, K., Onodera, S.-i., Saito, M., Nagano, Y., Yamazaki, M., & Nang, Y. W. (2025). Hydrological Modeling of the Chikugo River Basin Using SWAT: Insights into Water Balance and Seasonal Variability. Sustainability, 17(15), 7027. https://doi.org/10.3390/su17157027

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop