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Article

The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan

by
Bing Zhang
1,2,*,
Jingyan Han
3,
Jianbo Liu
1 and
Yong Zhao
4,*
1
Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
2
Earth and Environmental System, Graduate School of Science and Engineering, University of Toyama, Toyama 930-8555, Japan
3
College of Water Conservancy Engineering, Tianjin Agricultural University, Tianjin 300392, China
4
State Key Laboratory of Water Cycle and Water Sercurity, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Authors to whom correspondence should be addressed.
Hydrology 2025, 12(7), 187; https://doi.org/10.3390/hydrology12070187
Submission received: 10 June 2025 / Revised: 4 July 2025 / Accepted: 8 July 2025 / Published: 9 July 2025
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Abstract

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m3/s, 60.37 m3/s, 41.40 m3/s, 38.33 m3/s, and 18.72 m3/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions.

1. Introduction

Climate change affects regional and global hydrological processes (e.g., precipitation, runoff). The coastal zone, connecting terrestrial and marine environments, is the most productive and valuable ecosystem, but is sensitive and vulnerable to climate change. Under climate change, coastal zones will face changes in the frequency, intensity, spatial extent, and duration of marine heatwaves [1], and more severe compound flooding [2]. In addition, coastal water components, such as river discharge and submarine groundwater discharge (SGD), are the main sources of water flow and nutrients to the sea [3], linking the terrestrial and marine ecosystems. Hydrological processes are key to understanding the effects of climate change on coastal zones.
As the temperature increased, the water-holding capacity of air increased by approximately 7% per 1 °C, leading to an increase in water vapor in the atmosphere [4]. This rise in moisture altered the amount, frequency, and type (snow versus rain) of precipitation. Temperature has a significant relationship with rainfall variation patterns [5]. Over the past half-century, the ratio of snowfall to total precipitation (S/P) has decreased in many areas of the United States, ranging from −1.55% to −2.69% per decade [6]. Furthermore, a warming climate produced more intense precipitation events. Climate change altered the distribution of precipitation [7,8], changed river discharge [9], and intensified regional water scarcity [10]. Thus, understanding precipitation types is fundamental to revealing the impact of climate change on hydrological processes.
River discharge is a key process of water circulation and an important water resource for humans and ecosystems, connecting land and ocean in coastal zones. Rivers are available for agricultural and domestic water supply, natural resources, and trade [11], linking nature, life, and civilization [12]. Climate change affected freshwater in all seasons, particularly in winter [13]. With warming weather, more precipitation occurs as rain instead of snow, meaning the snow melts earlier, which leads to earlier spring snowmelt floods [14]. Climate change enhanced the variability of river flow. A warming climate affects the magnitude and timing of river floods [15]. The increased variability in river discharge was simulated by climate models and was projected to continue in the future. There was increased runoff and risk of flooding in early spring but an increased risk of drought in summer, especially over continental areas [4].
Understanding river discharge changes is a basis for analyzing the impact of climate change on the water cycle and adapting water resource management. Statistical and hydrological models have been applied to attribute the river discharge variations to human activities and climate change [16,17]. Budyko-type models, with precipitation (P), evapotranspiration (ET), river discharge (R), and hydrological conditions, have been efficient methods to analyze the runoff variations [9,18]. However, the Budyko and physical-based hydrological models need monitoring data to calculate the reliable attribution of river discharge changes. The Structural Equation Model (SEM), one of the widely used statistical methods, enables a good understanding of relatively complex models with mediating variables [19]. In comparison to Budyko and physical models, SEM served as an efficient approach for analyzing the contribution of climate change to variations in river discharge. Specifically, SEM offers advantages by enabling the incorporation of multiple mediating factors and providing a more nuanced analysis of causal relationships within complex systems.
Coastal rivers are typical, critical, and vulnerable to climate change. Toyama Prefecture, located in central Japan, is an ideal coastal area for studying climate change. The “Snow Corridor” in the Northern Alps is one of the most famous landmarks in Toyama. The snow is particularly deep where it drifts into valleys on the slopes to the west. However, an upward trend of the annual maximum daily precipitation has already been observed in Japan [20]. Toyama Prefecture has shown a rapid response to climate change, with a recent reduction in the perpetual snow coverage on top of the nearby mountains [21,22]. Influenced by the Asian monsoon, annual snowfall on the western Japanese coasts has decreased by 50–60% since the 1980s [23]. Moreover, Toyama Bay, a semi-enclosed bay in central Japan, is connected to the Sea of Japan at its northern boundary. The river flow, as well as the fresh submarine groundwater discharge (FSGD), is the link between the land and the sea [24]. Water circulation in Toyama has been extensively studied, including the water balance [25], the interaction between precipitation, groundwater, and FSGD [21,26], etc. Moreover, the impact of precipitation type on FSGD [23] and the river flood hazards caused by typhoons and frontal rains [27] have been documented in the literature. However, the quantitative interplay between precipitation and river discharge in the dynamic coastal environments remains poorly analyzed. The impact of precipitation pattern and temperature changes on river discharge, in particular, is still not clearly understood.
To better understand the impacts of precipitation type and temperature changes on river discharge, over 30 years of data pertaining to air temperature, precipitation (including rainfall and snowfall), and river discharge was analyzed. Notably, SEM provides distinct advantages by facilitating the integration of multiple mediating variables and offering a more sophisticated analysis of causal relationships within complex hydrological systems in coastal zones. Thus, the hypothesis of this research was that the precipitation pattern and river discharge will change as the warming climate impacts water circulation. The rescaled adjusted partial sums (RAPS) method, Mann–Kendall (MK) non-parametric test, and SEM were applied to determine the trends and relationships between air temperature, precipitation (snowfall, rainfall), and river discharge. The objectives of this research included (1) the changes in precipitation from 1939 to 2023 and snowfall depth from 1953 to 2023 in Toyama Prefecture; (2) the characteristics of snowfall liquid water equivalent (SFE) and the shift year of snowfall depth, determined with RAPS and the MK test; (3) the change in annual and monthly air temperature; (4) the annual and inter-annual changes in river discharge; and (5) attributing precipitation and temperature to changes in river discharge using the SEM. The impact of climate change on precipitation type, river discharge changes, and the coastal region was discussed to adapt the water resource management for the changing environment.

2. Materials and Methods

2.1. Study Area

Toyama Prefecture, alongside the Sea of Japan, is located in central Japan (Hokuriku region). Toyama is bordered by Ishikawa Prefecture to the west, Niigata to the northeast, Nagano to the southeast, Gifu to the south, and the Sea of Japan to the north (Figure 1). Toyama has a humid subtropical and humid continental climate, with chilly winters and warm summers. The annual precipitation at Toyama is 2296 mm, increasing to 4000 mm in the eastern mountains. The precipitation exhibits a distinctive peak during the summer months. The river flow to the sea is 364 m3/s [25]. The total amount of FSGD flowing into Toyama Bay was estimated to be 32 m3/s in 2001–2003 and 38 m3/s in 2018 [22]. The annual average air temperature is 14 °C. The annual potential evapotranspiration is 765 mm [22]. The plain area includes the Tonami plain. The Tateyama Mountains (3015 m a.s.l.) in the southeastern area are the highest mountains in Toyama.
There are five rivers that terminate at Toyama Bay (Table 1). The main rivers are Oyabe River, Shou (Shokawa) River, Jinzu River, Joganji River, and Kurobe River from the west to the east (Figure 1). The Oyabe River rises from Mount Daimon (1572 m a.s.l.) on the border of Ishikawa Prefecture and enters the sea. The length of Oyabe River is 68 km, and the basin is 682 km2. The source of Shou River is Mount Eboshi (1625 m a.s.l.). The river enters Toyama Bay after flowing for 115 km. The Tonami plain is formed by the Shou and Oyabe Rivers [28]. The Jinzu River flows from Mount Kaore (1626 m a.s.l.). It is 120 km in length and has a watershed of 2720 km2. The waterway of Jinzu River was constructed in the west of the city to avoid floods. The Joganji River originates in Mount Kitanomata (2662 m a.s.l.) in the southeastern area of the city of Toyama. The river is 56 km in length and the basin size is 368 km2. The Kurobe River rises from Mount Washiba (2924 m a.s.l.). It is 86 km in length and has a watershed of 689 km2. With a strong flow rate and a steep gradient, the Kurobe River has good conditions for hydroelectricity. The Kurobe Dam is the tallest dam in Japan. The river water discharge fluctuates seasonally and annually in response to precipitation patterns. The peak period of river discharge is in the summer months.

2.2. Data Sources

The climatic datasets were downloaded from the website of the Japan Meteorological Agency (JMA) (http://www.jma.go.jp/jma/index.html, last accessed on 27 February 2025), since climatic datasets with long time series are available from the website. The daily air temperature and precipitation data spans 1940 to 2023. The daily snowfall data (snowfall depth, cm) spans 1953 to 2023. Observations of precipitation (including rainfall and snowfall), air temperatures (maximum, mean, and minimum) at Toyama weather station were used in our analyses. These high-quality and long-term observation data have been subjected to quality control, which includes homogeneity testing and adjustments to assure their reliability.
The snow water equivalent (SWE), typically estimated from snow depth measurements, is a widely recognized term used to represent the liquid equivalent of snow cover [29,30]. However, determining SWE for measurements takes time. In this study, the liquid water equivalent of newly fallen snow on each day was defined as the precipitation totals (P) on days when newly fallen snow was recorded [31]. To distinguish from the SWE, the snowfall liquid water equivalent (SFE) was defined as being equal to P on days when S > 0, and it was set to zero when S = 0 [31], although the SFE may overestimate the proportion of precipitation classified as snowfall, since some precipitation events involve a mix of snow and rain [6]. However, the SFE is a parameter widely used to characterize the liquid water equivalent of newly fallen snow [6,31]. The snowfall depth (S) and SFE will be used to measure the snowfall. The snowfall depth data are derived from observations conducted by the JMA, serving as a key indicator for characterizing both the long-term trend and the shift year. The SFE was systematically analyzed to examine its relationship with climatic parameters and its impact on river discharge.
The daily river discharge data was obtained from the Water Information System (WIS), Ministry of Land, Infrastructure, Transport and Tourism (http://www1.river.go.jp/, last accessed on 27 February 2025). The selected gauges covered most of the river basin. The time series of river discharge datasets spans 1985 to 2020. Data on flood disaster events from 1940 to 2020 were obtained from Uozu City Hall, Toyama Prefecture (https://www.pref.toyama.jp, last accessed on 27 February 2025), combining the disaster survey report (https://www.jma-net.go.jp/toyama/_topics/sokuhou.html, last accessed on 27 February 2025). There were 10 flood disaster events (1952, 1953, 1956, 1958, 1959, 1961, 1969, 1976, 1983, and 1987) from 1940 to 1992; however, 11 flood events (1996, 1998, 2001, 2003, 2005, 2010, 2012, 2014, 2016, 2017, and 2018) occurred from 1993 to 2020.

2.3. Statistics Methods

The statistical characteristics and linear regression of precipitation and river discharge were carried out using the IBM SPSS Statistics 20 software. Five-year moving average values or standard deviations (s.d.) were analyzed to show the trends and variations in air temperature, precipitation, and river flow. The correlation analysis was carried out by the corrplot package in R (version 4.3.3).

2.3.1. Rescaled Adjusted Partial Sums (RAPS) Method

To identify potential shifts in temporal trends, we employed the rescaled adjusted partial sums (RAPS) method. RAPS, a well-established technique in hydrological analysis [32]—frequently applied to studies of climate parameters [32], streamflow [33], and water temperature [34]—requires only the mean and standard deviation of the time series under investigation. The method operates by generating a cumulative deviation series derived from the original continuous data. Visual inspection of the RAPS sum, computed from these deviations, facilitates the detection of anomalous data points and other abrupt changes in the series [35]. The RAPS statistic is calculated as follows:
R A P S k = ∑ t = 1 k Y t   − Y ¯ S y
where Y t represents the individual data point at time t, Y ¯ denotes the mean of the time series, S y is the standard deviation, and k signifies the total number of observations. The summation is performed iteratively from t = 1 to k .

2.3.2. Mann–Kendall (MK) Test

The Mann–Kendall (MK) test is a non-parametric statistical test employed to detect changes in trends and the mutation points of time series [36], widely used to assess trends in hydroclimatic data [37,38]. In this study, the MK test was used to detect whether a trend in the rainfall time series was statistically significant at a 95% confidence level. The MK test was further used to test sequence mutation.

2.3.3. Structural Equation Model (SEM)

Structural Equation Model (SEM) is a multivariate data statistical analysis method for analyzing complex relationships among constructs and indicators [39]. Amongst other advantages, SEM allows a good understanding of relatively complex models with mediating variables [19]. Thus, SEM is particularly useful to analyze the direct and indirect effects of multiple variables [40,41]. We hypothesized the influence of precipitation pattern and air temperature on the river discharge. The SEM with the latent variables (Precipitation, P; temperature, T; river discharge, R) and observed variables (rainfall, snowfall, etc.) was constructed by the packages “plspm” and “lavaan” in R (version 4.3.3). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the pathways and loadings. Although the SEM was simple and useful for understanding the impact of climate change on the changes in river discharge, we might miss some effects from excluded variables, especially the underlying surface in the Budyko framework.

3. Results

3.1. Changes in Precipitation and Snowfall

Rainfall and snowfall were the main types of precipitation. The annual precipitation increased non-significantly; however, the annual snowfall depth decreased significantly. The average annual precipitation amount was 2331 mm from 1939 to 2023 (Figure 2a). The average annual snowfall depth was 359 cm from 1953 to 2023 in Toyama Prefecture. Furthermore, the mean snowfall was 386 cm from 1953 to 2000, but this declined dramatically to 286 cm from 2001 to 2023 (Figure 2b). The annual precipitation increased slightly, but snowfall depth decreased dramatically in Toyama. Changes in precipitation and snowfall depth were identified using the RAPS method. Analysis of precipitation records from 1950 to 2023 revealed statistically distinct shifts in 1955, 1966, and 1997 (Figure 2c). The mean annual precipitation for the subperiods 1955–1966, 1967–1997, and 1998–2023 was 2531 mm, 2217 mm, and 2435 mm, respectively. Student’s t-test indicated significant differences in mean precipitation between these subperiods (p < 0.01). Regarding snowfall depth, shifts were detected in 1955 and 1986 (Figure 2d). Average snowfall depth decreased from 452 cm during 1955–1986 to 260 cm during 1987–2023. This difference in mean snowfall depth between the two subperiods was statistically significant (p < 0.01).
Snowfall was a key parameter of precipitation in Toyama Prefecture. The variations in snowfall depth and the snowfall liquid water equivalent (SFE) are illustrated in Figure 3a. The trends and variations in snowfall depth and SFE were similar overall. However, from 1990 onward, the values of SFE were consistently higher than those of snowfall depth. This discrepancy may be attributed to an overestimation of SFE. The Mann–Kendall (MK) test was applied by R codes to analyze the temporal shift in precipitation patterns, especially the snowfall tipping point. The statistic value UF of snowfall decreased from 1953 to 2023, while UB increased over these years. The point at which the UF and UB values crossed was the year 1992, indicating that the shift year for snow was 1992 (Figure 3b). The shift year of snowfall determined using the MK test coincided with the trend in the 5-year moving average of snowfall (Figure 2d). As snowfall was the typical parameter used to indicate precipitation pattern, it was clear that the precipitation pattern at Toyama Prefecture changed in 1992. This shift year will later be used as a turning point to analyze the changes in temperature and river discharge.

3.2. Increased Air Temperature

The annual air temperature, including the lowest (lowT), highest (highT), and mean temperature (meanT), increased significantly (Figure 4a) (n = 76, p < 0.001). The average annual air temperature increased about 0.2 °C per decade. The average annual air temperature was 13.5 °C from 1940 to 1999, while the average value of air temperature was 14.6 °C after the 2000s. Changes in air temperature were identified using the RAPS method. Analysis of mean air temperature dataset from 1940 to 2023 revealed statistically distinct shifts in 1992 (Figure 4b), which coincided with the MK test of snowfall depth (Figure 3b). The average air temperature increased from 13.4 °C during 1940–1992 to 14.6 °C during 1993–2023. This difference in mean air temperature between the two subperiods was statistically significant (p < 0.01).
The air temperature increased each month. The monthly weather, especially in late winter and early spring (January, February, and March), was significantly warmer in 2001–2023 than the temperature in 1940–2000 (Figure 5). The mean air temperature in February increased the most (60.8%), followed by January (46.2%) and March (34.2%) (Figure 5a). The variation in the highest air temperature value showed the same trend as the mean air temperatures. Furthermore, the highest air temperature in February increased the most (26.8%), followed by March (23.5%) and January (17.8%) (Figure 5b). The lowest air temperature, in particular, increased dramatically in winter and spring. The temperature increased by 119.3%, 111.9%, and 101.4% in January, February, and March, respectively (Figure 5c). Consequently, the weather in Toyama was becoming warmer, especially in late winter and early spring.

3.3. Changes in River Discharge

The river discharge, rainfall, and snowfall are shown in Figure 6. The runoff of the Jinzu River was the largest, with an average discharge of 182.52 m3/s. The average discharges of Oyabe River, Kurobe River, Shou River, and Joganji River were 60.37 m3/s, 41.40 m3/s, 38.33 m3/s, and 18.72 m3/s, respectively. The variation trends for most rivers were the same, except for the Kurobe River (Figure 6a). The Kurobe River discharge increased dramatically to the second-largest value between 1999 and 2002. The discharge of the Kurobe River decreased after 2002; however, the runoff of the river was larger than that of the Joganji River from 1999 to 2020. The 5-year moving average standard deviation of the Jinzu River was the largest (Figure 6b). The average standard deviations of the Shou River were the second-largest from 1990 to 1995. The standard deviations of the Kurobe River increased after 1995, peaked in 2007, and then decreased. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020) (Figure 6b). Furthermore, the analysis of cumulative flood events and precipitation is presented in Figure 6c. The tipping year of flood events was 1996, which was four years later compared to the shift year of snowfall depth (1992, Figure 3b). The changes in the river discharge regime increased the flood frequency.
The spatiotemporal variation in river discharge is linked to the river network and precipitation. Compared to 1985–2000, the monthly runoff of most rivers increased in spring during 2001–2020. The Kurobe River flux increased in spring, summer, and early autumn, but decreased in November and winter (Table 2). The Joganji River discharge decreased in January, June, and July but increased in other months. The Jinzu River flux decreased in May and September but increased in other months. The Shou River discharge decreased in June but increased in other months. The Oyabe River discharge decreased in January, February, May, June, and July but increased in other months. Most of the river discharge increased in Toyama. As the climate grew warmer, most of the river discharge increased, especially in summer, while some of the river discharge decreased in winter and spring. The increasing river discharge in summer may result in the occurrence of a river flood.

3.4. Relationship Between Precipitation, Temperature, and River Discharge

To better understand the attribution of precipitation and temperature to river discharge, PLS-SEM with the latent variables (precipitation, P; temperature, T; river discharge, R) and observed variables (rainfall, snowfall, etc.) was illustrated (Figure 7). The latent variable of temperature (T) was significantly correlated with minimum (minT) and maximum daily air temperature (maxT). The loading between latent variable P and rainfall (0.94) was larger than that of P and snowfall (0.47). In contrast, the latent variable of river discharge (R) indicated the variations in most of the river discharge, with statistically significant correlation coefficients. Furthermore, the SEM showed the pathways of climatic factors impacting the river discharge. The pathway showed a statistically significant positive relationship between precipitation pattern and river discharge with loading of 0.88. However, the loading of air temperature and river discharge was 0.10. The results of the SEM coincided with the above correlation analysis, but the SEM clarified the pathways between climate change and river discharge quantity, thus demonstrating that the precipitation dominated the changes in river discharge.
The SEM revealed that river discharge was primarily driven by precipitation, particularly rainfall. Air temperature, however, did not exert a dominant influence on river discharge. Analysis using the RAPS method identified shifts in both air temperature (1992) and snowfall depth (1986), with temporal proximities suggesting a potential link between rising temperatures and reduced snowfall accumulation. The observed shift in river flood disaster frequency occurred in 1996 (Figure 6c), coinciding with a precipitation shift identified in 1997. These findings reinforce the conclusion that precipitation is a key driver of river discharge. Consequently, in a warming climate, snowfall depth exhibits sensitivity to air temperature, manifesting as decreased snow accumulation coupled with increasing winter temperatures.

4. Discussion

4.1. Tipping Point of Precipitation Pattern

Snowfall was the typical parameter indicating the precipitation pattern in Toyama Prefecture. The variation in snowfall indicated the regional and global climate changes [42]. To characterize the critical threshold beyond which a system reorganizes, often abruptly and irreversibly, the tipping point was used to represent the potential for climate change to cause shifts, along with an abrupt threshold [43]. The RAPS method was employed to identify potential tipping points within the original time series data. It was applied to streamflow data from Bednja River (Croatia) [33] and water temperature in the river Danube (Serbia) [34]. This study suggested a possible shift in snowfall depth around 1986. However, the RAPS method detected a change in the mean air temperature in 1992 (Figure 4b). Meanwhile, the MK test also indicated that the shift year of snowfall was 1992 (Figure 3). Consequently, the tipping point of the precipitation pattern in Toyama was 1992, indicating the shift year for climate change.
Air temperature directly reflected the status of the climate, showing the warm or cold trends of climate change. According to the annual climatic datasets, the climate of Toyama was getting warmer. The annual air temperature in Toyama was increasing about 0.2 °C per decade (Figure 4). The increase in air temperature changed the form of precipitation from solid to liquid, causing the shift from snow to rain. As the seawater grew warmer, the monsoon variability influenced the local monthly snowfall on the Japan Sea side of central Japan through change in the explosive cyclone activity [44]. The warming weather, especially the increased lowest air temperature (Figure 4), changed the precipitation type in late winter and early spring. The snowfall decreased dramatically at a rate of −31.3 mm per decade (Figure 2), caused by the increased temperature. Furthermore, based on the RAPS, analysis of the relationship between temperature residuals and a previously established climate change index [34] may enable prediction of air temperature and snowfall depth at seasonal timescales.

4.2. Variation in River Discharge May Increase Flood Risk

The shift in the state of precipitation from solid to liquid influenced the magnitude and time of the river discharge. The decreasing S/P ratio resulted in the earlier occurrence of snowmelt recharge in runoff [45], changing the time of the spring runoff [6]. Furthermore, the warmer temperatures led to earlier spring snowmelt [15], causing the earlier flood timing in North America and Europe [46]. These changes in the timing and magnitude of river discharge affected the hydrological processes [47], increased the flood frequencies and flood risk [48], and may have caused the water shortage in summer [6]. The results of this research showed the increasing trend of river discharge in spring and winter and decreasing trend of river discharge in summer (Figure 6 and Table 1).
The warmer temperature caused light snowfall, as well as the upward movement of river flow in winter and spring. The rainfall significantly increased in late spring (March, April), summer (July, August), late autumn (October) and winter (November, December), but decreased in early summer (May, June) and September (Figure 4a). The increased rainfall in winter and earlier snowmelt in spring (Figure 5) changed the timing and led to earlier river discharge (Table 2), may have caused more frequent flooding, and decreased river discharge. There were 12 flood disaster events from 1940 to 2000; however, 9 flood events occurred during 2001 to 2022 in the records from Uozu City Hall, Toyama Prefecture. The percentage of flood occurrence increased from 19% to 41% (Figure 6b,c).

4.3. Vulnerability of Water Resources in Coastal Region

Coastal zones, the link between land and ocean, are a critical and vulnerable ecosystem threatened by climate change. The rivers and streams represent vital renewable freshwater resources. The changes in magnitude and the timing of rivers indicate the increasing water scarcity and should highlight the necessity of raising the awareness of climate change and maintaining sustainable water resources [10]. Moreover, the river was the link between landmasses and the ocean. The outflow of the river into the Toyama Bay was 115 × 108 m3/yr, which was about four times that of the submarine groundwater discharge (33 × 108 m3/yr) to the sea [25]. The riverine nutrient had a significant impact on the coastal surface water column [21,49]. The river flow and nutrient fluxes could potentially change the water quality and aquatic environment in coastal zones, even increasing the nutrient limitation in coastal waters [50]. The changes in the timing of river discharge may influence the coastal and marine ecosystem.
Submarine groundwater discharge (SGD) is another critical link connecting the land and ocean in the coastal zones. Climate change could impact groundwater recharge and supply in regions with seasonal snow cover [51]. Furthermore, as the climate grew warmer, the residence time of the fresh submarine groundwater discharge (FSGD) in Toyama Bay was shortened by 25%, and the FSGD volume increased by 13–25% as a consequence of snowfall changing to rainfall in winter [23]. The shift from snowfall to rainfall in coastal zones [52] altered the river discharge systems as well as the SGD. The climate change-induced variation in the flux of water and nutrients may increase the vulnerability of flood risk, as well as freshwater resources in coastal zones globally.

4.4. Limitations and Future Work

Climate is one of the key factors affecting river discharge. However, there are other factors that impact the river discharge, including evapotranspiration, land-use changes [53], anthropogenic climate change [54], etc. The MK test was a typical and classical method to detect the tipping point. The RAPS method effectively identified shifts in precipitation and air temperature through visual analysis. Thus, correlation analysis of RAPS-derived values would be employed to further elucidate the relationships between precipitation, air temperature, and river discharge [33,34]. Although an SEM approach was used to better understand the pathways through which climatic factors impact river discharge, the model remained relatively simple and was not suitable for predictive applications (Figure 7). The key hydrological processes (e.g., evapotranspiration, land-use changes) will be considered in the SEM in future work. Furthermore, the machine learning methods have garnered substantial recognition within hydrology and water resource applications [55].
In future, we will aim to expand our focus on river discharge-related events by incorporating data from additional meteorological stations and river gauge stations. This expanded dataset will enable more applied analyses and inform actionable policies. Furthermore, we plan to analyze regime shifts in river systems and their cascading effects on coastal hydrological processes by integrating advanced machine learning techniques with state-of-the-art hydrological models. These enhancements will provide a more comprehensive understanding of the complex interactions between the climate, river discharge, and coastal dynamics, ultimately supporting improved water resource management and policy development.

5. Conclusions

The datasets of air temperature, precipitation forms (rainfall and snowfall), and river discharge, spanning over 30 years, were analyzed to understand the effects of climate change in Toyama Prefecture, central Japan. The coastal zone of Toyama Bay has been growing warmer. Air temperature increased significantly at a rate of 0.2 °C per decade. Furthermore, the temperature increase during late winter (January–February) and early spring (March) was greater than in other months. The rainfall amounts increased in late winter and spring, but snowfall declined significantly. The tipping point for the shift in precipitation patterns occurred in 1992. The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m3/s, 60.37 m3/s, 41.40 m3/s, 38.33 m3/s, and 18.72 m3/s, respectively. The variability (standard deviation) in the Kurobe River’s discharge increased sharply after 1995, with the peak value occurring in 2007. Most rivers experienced dramatic increases in discharge during spring, late autumn, and early winter, but decreases were observed in July and September. The warmer climate has altered the timing and concentration periods of river discharge.
As warming weather and rising sea surface temperatures continue, precipitation along the Japan Sea coast is expected to increase. Light snowfall events have become more common in winter. Moreover, increased rainfall and earlier snowmelt have affected both the magnitude and timing of river floods. The warming climate has changed river discharge patterns, potentially increased the frequency of river floods, and caused water shortages in summer. These changes highlight the need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions.

Author Contributions

Conceptualization, B.Z., Y.Z. and J.L.; methodology, B.Z. and J.H.; software, B.Z.; writing—original draft preparation, B.Z.; writing—review and editing, J.H., Y.Z. and B.Z.; funding acquisition, B.Z., J.L. and J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (nos. 41971037 and 42201126), the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) (no. IWHR-SKL-KF202209), and the Scientific Research Project of Tianjin Municipal Education Commission (no. 2019KJ091).

Data Availability Statement

All data from this study have been included in the text.

Acknowledgments

The authors sincerely thank J.Z. and M.S. for the suggestions and the data of flood disaster events.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022—Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Cambridge University Press: Cambridge, UK, 2023; ISBN 978-1-009-32584-4. [Google Scholar]
  2. Xu, K.; Zhuang, Y.; Bin, L.; Wang, C.; Tian, F. Impact Assessment of Climate Change on Compound Flooding in a Coastal City. J. Hydrol. 2023, 617, 129166. [Google Scholar] [CrossRef]
  3. Tait, D.R.; Santos, I.R.; Lamontagne, S.; Sippo, J.Z.; McMahon, A.; Jeffrey, L.C.; Maher, D.T. Submarine Groundwater Discharge Exceeds River Inputs as a Source of Nutrients to the Great Barrier Reef. Environ. Sci. Technol. 2023, 57, 15627–15634. [Google Scholar] [CrossRef] [PubMed]
  4. Trenberth, K.E. Changes in Precipitation with Climate Change. Clim. Res. 2011, 47, 123–138. [Google Scholar] [CrossRef]
  5. Pattnayak, K.C.; Awasthi, A.; Sharma, K.; Pattnayak, B.B. Fate of Rainfall over the North Indian States in the 1.5 and 2 °C Warming Scenarios. Earth Space Sci. 2023, 10, e2022EA002671. [Google Scholar] [CrossRef]
  6. Feng, S.; Hu, Q. Changes in Winter Snowfall/Precipitation Ratio in the Contiguous United States. J. Geophys. Res. Atmos. 2007, 112, D15109. [Google Scholar] [CrossRef]
  7. Putnam, A.E.; Broecker, W.S. Human-Induced Changes in the Distribution of Rainfall. Sci. Adv. 2017, 3, e1600871. [Google Scholar] [CrossRef]
  8. Lenderink, G.; Fowler, H.J. Hydroclimate: Understanding Rainfall Extremes. Nat. Clim. Change 2017, 7, 391–393. [Google Scholar] [CrossRef]
  9. Hu, Y.; Zhou, Y.; Wang, Y.; Lu, F.; Xiao, W.; Hou, B.; Yu, Y.; Liu, J.; Xue, W. Impacts of Precipitation Type Variations on Runoff Changes in the Source Regions of the Yangtze and Yellow River Basins in the Past 40 Years. Water 2022, 14, 4115. [Google Scholar] [CrossRef]
  10. Gudmundsson, L.; Seneviratne, S.I.; Zhang, X. Anthropogenic Climate Change Detected in European Renewable Freshwater Resources. Nat. Clim. Change 2017, 7, 813–816. [Google Scholar] [CrossRef]
  11. Wang, Y.; Borthwick, A.G.L.; Ni, J. Human Affinity for Rivers. River 2022, 1, 4–14. [Google Scholar] [CrossRef]
  12. Wang, H.; He, G. Rivers: Linking Nature, Life, and Civilization. River 2022, 1, 25–36. [Google Scholar] [CrossRef]
  13. Cotner, J.B.; Powers, S.M.; Sadro, S.; McKnight, D. Whither Winter: The Altered Role of Winter for Freshwaters as the Climate Changes. J. Geophys. Res. Biogeosci. 2022, 127, e2021JG006761. [Google Scholar] [CrossRef]
  14. Merz, B.; Blöschl, G.; Vorogushyn, S.; Dottori, F.; Aerts, J.C.J.H.; Bates, P.; Bertola, M.; Kemter, M.; Kreibich, H.; Lall, U.; et al. Causes, Impacts and Patterns of Disastrous River Floods. Nat. Rev. Earth Environ. 2021, 2, 592–609. [Google Scholar] [CrossRef]
  15. Blöschl, G.; Hall, J.; Parajka, J.; Perdigão, R.A.P.; Merz, B.; Arheimer, B.; Aronica, G.T.; Bilibashi, A.; Bonacci, O.; Borga, M.; et al. Changing Climate Shifts Timing of European Floods. Science 2017, 357, 588–590. [Google Scholar] [CrossRef] [PubMed]
  16. Sun, X.; Dong, Q.; Zhang, X. Attribution Analysis of Runoff Change Based on Budyko-Type Model with Time-Varying Parameters for the Lhasa River Basin, Qinghai–Tibet Plateau. J. Hydrol. Reg. Stud. 2023, 48, 101469. [Google Scholar] [CrossRef]
  17. Huang, T.; Liu, Y.; Jia, Z.; Zou, J.; Xiao, P. Applicability of Attribution Methods for Identifying Runoff Changes in Changing Environments. Sci. Rep. 2024, 14, 26100. [Google Scholar] [CrossRef]
  18. Thomas, B.F.; Nanteza, J. Global Assessment of the Sensitivity of Water Storage to Hydroclimatic Variations. Sci. Total Environ. 2023, 879, 162958. [Google Scholar] [CrossRef]
  19. de Franca Doria, M.; Pidgeon, N.; Hunter, P. Perception of Tap Water Risks and Quality: A Structural Equation Model Approach. Water Sci. Technol. 2005, 52, 143–149. [Google Scholar] [CrossRef]
  20. Xu, Z.X.; Takeuchia, K.; Ishidaira, H. Monotonic Trend and Step Changes in Japanese Precipitation. J. Hydrol. 2003, 279, 144–150. [Google Scholar] [CrossRef]
  21. Hatta, M.; Zhang, J. Temporal Changes and Impacts of Submarine Fresh Groundwater Discharge to the Coastal Environment: A Decadal Case Study in Toyama Bay, Japan. J. Geophys. Res. Oceans 2013, 118, 2610–2622. [Google Scholar] [CrossRef]
  22. Zhang, B.; Zhang, J.; Yoshida, T. Temporal Variations of Groundwater Tables and Implications for Submarine Groundwater Discharge: A 3-Decade Case Study in Central Japan. Hydrol. Earth Syst. Sci. 2017, 21, 3417–3425. [Google Scholar] [CrossRef]
  23. Katazakai, S.; Zhang, J. A Shift from Snow to Rain in Midlatitude Japan Increases Fresh Submarine Groundwater Discharge and Doubled Inorganic Carbon Flux over 20 Years. Environ. Sci. Technol. 2021, 55, 14667–14675. [Google Scholar] [CrossRef]
  24. Zhang, J.; Satake, H. The Chemical Characteristics of Submarine Groundwater Seepage in Toyama Bay, Central Japan. In Land and Marine Hydrogeology; Taniguchi, M., Wang, K., Gamo, T., Eds.; Elsevier: Amsterdam, The Netherlands, 2003; pp. 45–60. ISBN 978-0-444-51479-0. [Google Scholar]
  25. Ito, T.; Fujii, S. The Water Balance of Ground-Water Reservoir in the Toyama Basin. Mem. Toyama Geogr. Soc. 1993, 10, 63–74. [Google Scholar]
  26. Okakita, N.; Iwatake, K.; Hirata, H.; Ueda, A. Contribution of Precipitation to Groundwater Flow Systems in Three Major Alluvial Fans in Toyama Prefecture, Japan: Stable-Isotope Characterization and Application to the Use of Groundwater for Urban Heat Exchangers. Hydrogeol. J. 2019, 27, 345–362. [Google Scholar] [CrossRef]
  27. Ishikawa, S.; Kure, S.; Yagi, R.; Priyambodho, B. Flood Hazard Evaluation for Rivers in Toyama Prefecture, Japan. In Proceedings of the 22nd IAHR APD Congress, Sapporo, Japan, 14–17 September 2020. [Google Scholar]
  28. Kamishima, T.; Takeuchi, A. Late Quaternary Geomorphology of the Tonami Plain and Activity of the Tonami-Heiya Fault Zone, Toyama Prefecture, Central Japan. Int. J. Geosci. 2016, 7, 962–976. [Google Scholar] [CrossRef]
  29. Jonas, T.; Marty, C.; Magnusson, J. Estimating the Snow Water Equivalent from Snow Depth Measurements in the Swiss Alps. J. Hydrol. 2009, 378, 161–167. [Google Scholar] [CrossRef]
  30. Kuribayashi, M.; Noh, N.J.; Saitoh, T.M.; Tamagawa, I.; Wakazuki, Y.; Muraoka, H. Comparison of Snow Water Equivalent Estimated in Central Japan by High-Resolution Simulations Using Different Land-Surface Models. SOLA 2013, 9, 148–152. [Google Scholar] [CrossRef]
  31. Knowles, N.; Dettinger, M.D.; Cayan, D.R. Trends in Snowfall versus Rainfall in the Western United States. J. Clim. 2006, 19, 4545–4559. [Google Scholar] [CrossRef]
  32. Đurin, B.; Kranjčić, N.; Kanga, S.; Singh, S.K.; Sakač, N.; Pham, Q.B.; Hunt, J.; Dogančić, D.; Di Nunno, F. Application of Rescaled Adjusted Partial Sums (RAPS) Method in Hydrology—An Overview. Adv. Civ. Archit. Eng. 2022, 13, 58–72. [Google Scholar] [CrossRef]
  33. Đurin, B.; Plantak, L.; Bonacci, O.; Di Nunno, F. A Unique Approach to Hydrological Behavior along the Bednja River (Croatia) Watercourse. Water 2023, 15, 589. [Google Scholar] [CrossRef]
  34. Basarin, B.; Lukić, T.; Pavić, D.; Wilby, R.L. Trends and Multi—Annual Variability of Water Temperatures in the River Danube, Serbia. Hydrol. Process. 2016, 30, 3315–3329. [Google Scholar] [CrossRef]
  35. Šrajbek, M.; Đurin, B.; Sušilović, P.; Singh, S.K. Application of the RAPS Method for Determining the Dependence of Nitrate Concentration in Groundwater on the Amount of Precipitation. Earth 2023, 4, 266–277. [Google Scholar] [CrossRef]
  36. Burn, D.H.; Hag Elnur, M.A. Detection of Hydrologic Trends and Variability. J. Hydrol. 2002, 255, 107–122. [Google Scholar] [CrossRef]
  37. Zhang, Y.; Liang, C. Analysis of Annual and Seasonal Precipitation Variation in the Qinba Mountain Area, China. Sci. Rep. 2020, 10, 961. [Google Scholar] [CrossRef]
  38. Wei, Q.; Sun, C.; Wu, G.; Pan, L. Haihe River Discharge to Bohai Bay, North China: Trends, Climate, and Human Activities. Hydrol. Res. 2016, 48, 1058–1070. [Google Scholar] [CrossRef]
  39. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Danks, N.P.; Ray, S. An Introduction to Structural Equation Modeling. In Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R; Springer International Publishing: Cham, Switzerland, 2021; pp. 1–29. ISBN 978-3-030-80518-0. [Google Scholar]
  40. Tian, P.; Liu, S.; Zhao, X.; Sun, Z.; Yao, X.; Niu, S.; Crowther, T.W.; Wang, Q. Past Climate Conditions Predict the Influence of Nitrogen Enrichment on the Temperature Sensitivity of Soil Respiration. Commun. Earth Environ. 2021, 2, 251. [Google Scholar] [CrossRef]
  41. Meng, Y.; Li, S.; Wang, S.; Meiners, S.J.; Jiang, L. Scale-Dependent Changes in Ecosystem Temporal Stability over Six Decades of Succession. Sci. Adv. 2023, 9, eadi1279. [Google Scholar] [CrossRef]
  42. Han, J.; Liu, Z.; Woods, R.; McVicar, T.R.; Yang, D.; Wang, T.; Hou, Y.; Guo, Y.; Li, C.; Yang, Y. Streamflow Seasonality in a Snow-Dwindling World. Nature 2024, 629, 1075–1081. [Google Scholar] [CrossRef]
  43. Kopp, R.E.; Gilmore, E.A.; Shwom, R.L.; Adams, H.; Adler, C.; Oppenheimer, M.; Patwardhan, A.; Russill, C.; Schmidt, D.N.; York, R. ‘Tipping Points’ Confuse and Can Distract from Urgent Climate Action. Nat. Clim. Change 2024, 15, 29–36. [Google Scholar] [CrossRef]
  44. Yamashita, Y.; Kawamura, R.; Iizuka, S.; Hatsushika, H. Explosively Developing Cyclone Activity in Relation to Heavy Snowfall on the Japan Sea Side of Central Japan. J. Meteorol. Soc. Jpn. Ser. II 2012, 90, 275–295. [Google Scholar] [CrossRef]
  45. Li, Q.; Yang, T.; Qi, Z.; Li, L. Spatiotemporal Variation of Snowfall to Precipitation Ratio and Its Implication on Water Resources by a Regional Climate Model over Xinjiang, China. Water 2018, 10, 1463. [Google Scholar] [CrossRef]
  46. Fang, G.; Yang, J.; Li, Z.; Chen, Y.; Duan, W.; Amory, C.; De Maeyer, P. Shifting in the Global Flood Timing. Sci. Rep. 2022, 12, 18853. [Google Scholar] [CrossRef]
  47. Getirana, A.; Kumar, S.; Konapala, G.; Nie, W.; Locke, K.; Loomis, B.; Birkett, C.; Ricko, M.; Simard, M. Climate and Human Impacts on Hydrological Processes and Flood Risk in Southern Louisiana. Water Resour. Res. 2023, 59, e2022WR033238. [Google Scholar] [CrossRef]
  48. Swarnkar, S.; Mujumdar, P. Increasing Flood Frequencies under Warming in the West-Central Himalayas. Water Resour. Res. 2023, 59, e2022WR032772. [Google Scholar] [CrossRef]
  49. Liu, Q.; Charette, M.A.; Henderson, P.B.; McCorkle, D.C.; Martin, W.; Dai, M. Effect of Submarine Groundwater Discharge on the Coastal Ocean Inorganic Carbon Cycle. Limnol. Oceanogr. 2014, 59, 1529–1554. [Google Scholar] [CrossRef]
  50. Katazakai, S.; Zhang, J. A Quarter-Century of Nutrient Load Reduction Leads to Halving River Nutrient Fluxes and Increasing Nutrient Limitation in Coastal Waters of Central Japan. Environ. Monit. Assess. 2021, 193, 573. [Google Scholar] [CrossRef]
  51. Hyman-Rabeler, K.A.; Loheide II, S.P. Drivers of Variation in Winter and Spring Groundwater Recharge: Impacts of Midwinter Melt Events and Subsequent Freezeback. Water Resour. Res. 2023, 59, e2022WR032733. [Google Scholar] [CrossRef]
  52. Curtis, S. Means and Long-Term Trends of Global Coastal Zone Precipitation. Sci. Rep. 2019, 9, 5401. [Google Scholar] [CrossRef]
  53. Gunawardana, C.; McDonald, W. Impacts of Land Use Changes on Discharge and Water Quality in Rivers and Streams: Case Study of the Continental United States. JAWRA J. Am. Water Resour. Assoc. 2024, 60, 725–740. [Google Scholar] [CrossRef]
  54. Wang, H.; Liu, J.; Klaar, M.; Chen, A.; Gudmundsson, L.; Holden, J. Anthropogenic Climate Change Has Influenced Global River Flow Seasonality. Science 2024, 383, 1009–1014. [Google Scholar] [CrossRef]
  55. Tripathy, K.P.; Mishra, A.K. Deep Learning in Hydrology and Water Resources Disciplines: Concepts, Methods, Applications, and Research Directions. J. Hydrol. 2024, 628, 130458. [Google Scholar] [CrossRef]
Hydrology 12 00187 g001
Figure 1. The location of Japan (a); the selected study area within Japan (b); and the gauges, weather station, and rivers in Toyama Prefecture, central Japan (c).
Figure 1. The location of Japan (a); the selected study area within Japan (b); and the gauges, weather station, and rivers in Toyama Prefecture, central Japan (c).
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Hydrology 12 00187 g002
Figure 2. The annual precipitation (a) and snowfall depth (b); the rescaled adjusted partial sums (RAPS) of precipitation (c) and snowfall depth (d).
Figure 2. The annual precipitation (a) and snowfall depth (b); the rescaled adjusted partial sums (RAPS) of precipitation (c) and snowfall depth (d).
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Hydrology 12 00187 g003
Figure 3. The variation in snowfall depth and snowfall liquid water equivalent (a); the shift year of snowfall depth (b) using the MK test.
Figure 3. The variation in snowfall depth and snowfall liquid water equivalent (a); the shift year of snowfall depth (b) using the MK test.
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Hydrology 12 00187 g004
Figure 4. The annual mean (a) and the rescaled adjusted partial sums (RAPS) of air temperature (b).
Figure 4. The annual mean (a) and the rescaled adjusted partial sums (RAPS) of air temperature (b).
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Figure 5. The inter-annual variation in monthly air temperature, the mean air temperature (a), the highest air temperature (b), and the lowest air temperature (c).
Figure 5. The inter-annual variation in monthly air temperature, the mean air temperature (a), the highest air temperature (b), and the lowest air temperature (c).
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Hydrology 12 00187 g006
Figure 6. The annual variation in river discharge (a); the five-year moving average standard deviation of monthly river discharge and accumulated flood events from 1985 to 2020 (b); and the cumulative curve of flood events and precipitation (c).
Figure 6. The annual variation in river discharge (a); the five-year moving average standard deviation of monthly river discharge and accumulated flood events from 1985 to 2020 (b); and the cumulative curve of flood events and precipitation (c).
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Hydrology 12 00187 g007
Figure 7. Pathways of climatic factors (precipitation pattern, P; air temperature, T) impacting the river discharge (R). *, **, and *** indicate the correlation is significant at the 0.05, 0.01, and 0.001 level, respectively.
Figure 7. Pathways of climatic factors (precipitation pattern, P; air temperature, T) impacting the river discharge (R). *, **, and *** indicate the correlation is significant at the 0.05, 0.01, and 0.001 level, respectively.
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Table 1. Basic information on the rivers, river gauges, and weather station.
Table 1. Basic information on the rivers, river gauges, and weather station.
ItemsLength
(km)		Basin Area
(km2)		GaugesLongitudeLatitudeElevation
(m a.s.l.)
Kurobe River86689Aimoto137.5547° E36.8594° N123.8
Joganji River56368Kameiwa137.3430° E36.5756° N237.46
Jinzu River1202720Jinzu ohasi137.2041° E36.7014° N−0.09
Shou River1151180Daimon137.0436° E36.7350° N−0.04
Oyabe River68682Nagae136.9830° E36.7583° N−0.10
Weather station Toyama137.2016° E36.7083° N8.60
Table 2. The inter-annual variation in river discharge before and after the shift year 1992.
Table 2. The inter-annual variation in river discharge before and after the shift year 1992.
MonthKurobe RiverJoganji RiverJinzu River
1985–19921993–2020change1985–19921993–2020Change1985–19921993–2020Change
m3/s%m3/s%m3/s%
Jan6.4115.90148.13.907.4490.7142.70128.21−10.2
Feb6.1517.41183.04.127.2375.4148.23138.43−6.6
Mar8.1622.85180.06.6212.0281.5220.64220.960.1
Apr17.7545.24155.017.5029.1966.8278.18310.9911.8
May36.3597.04167.025.7144.1071.5190.85211.5910.9
Jun42.90100.56134.424.9631.5626.4168.72146.45−13.2
Jul56.20113.57102.134.7338.2410.1250.56259.613.6
Aug10.2141.88310.18.1618.49126.7128.23163.3827.4
Sep13.8231.53128.114.4517.4120.5212.07192.49−9.2
Oct10.0924.38141.710.4916.7859.9149.50159.726.8
Nov10.4022.83119.67.0714.95111.3136.79142.604.2
Dec7.1017.46145.94.278.99110.8131.53145.0310.3
Shou RiverOyabe RiverRiver Average
1985–19921993–2020change1985–19921993–2020change1985–19921993–2020change
m3/s%m3/s%m3/s%
Jan16.7628.7971.869.5569.12−0.647.8649.894.2
Feb22.0630.8039.668.7967.26−2.249.8752.234.7
Mar58.7063.347.971.9366.12−8.173.2177.065.3
Apr71.4772.451.456.7060.406.588.32103.6617.4
May26.6350.7090.456.9355.48−2.567.2991.7836.4
Jun24.3522.41−8.056.7454.67−3.663.5471.1312.0
Jul55.0260.7110.470.7666.56−5.993.45107.7415.3
Aug11.0035.19220.045.3356.3024.240.5963.0555.3
Sep40.5042.785.658.1758.11−0.167.8068.461.0
Oct20.9836.5574.242.3943.091.746.6956.1020.2
Nov16.9925.7251.453.8154.601.545.0152.1415.8
Dec19.8432.9266.062.7771.2213.545.1055.1222.2
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Zhang, B.; Han, J.; Liu, J.; Zhao, Y. The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan. Hydrology 2025, 12, 187. https://doi.org/10.3390/hydrology12070187

AMA Style

Zhang B, Han J, Liu J, Zhao Y. The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan. Hydrology. 2025; 12(7):187. https://doi.org/10.3390/hydrology12070187

Chicago/Turabian Style

Zhang, Bing, Jingyan Han, Jianbo Liu, and Yong Zhao. 2025. "The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan" Hydrology 12, no. 7: 187. https://doi.org/10.3390/hydrology12070187

APA Style

Zhang, B., Han, J., Liu, J., & Zhao, Y. (2025). The Impact of Shifts in Both Precipitation Pattern and Temperature Changes on River Discharge in Central Japan. Hydrology, 12(7), 187. https://doi.org/10.3390/hydrology12070187

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