ABSTRACT
Runoff is a pivotal ecohydrological cycle feature. Investigating watershed runoff change and its influencing factors from multi-temporal and spatial angles is crucial for water ecology control. The study analyzes hydrological and meteorological changes using the indicators of hydrological alteration and the range of variation approach (IHA–RVA) and RClimDex to explore their dynamic relationship. Finally, using the soil and water assessment tool to quantify climate and human contributions to runoff temporally and spatially, with validation using Budyko-based elasticity coefficients. Results showed that (1) most of the meteorological indices show an upward trend, a change attributable mainly to strong human activity and global warming. The overall hydrological indicators show a moderate degree of change (50.70%). (2) PRCPTOT (the annual total precipitation) and 30-day minimum demonstrate a negative correlation coefficient of 0.91 in the meteorological–hydrological response. (3) On annual/seasonal scales, human activities such as hydraulic projects and land use/cover changes (LUCC) dominate runoff changes. On a monthly scale, climate change prevails in March and November due to temperature/rainfall fluctuations, while human activities dominate other months. Spatially, climate change and LUCC mainly impact runoff in the southeast. The study offers references to improve water management in the Jialing River Basin, effectively addressing the negative impacts of human activities on runoff.
HIGHLIGHTS
The Budyko hypothesis and the soil and water assessment tool model were used for the attribution analysis of runoff changes.
A correlation analysis was conducted between hydrological and meteorological indices.
Conducting a spatiotemporal attribution analysis of runoff in the Jialing River Basin.
Quantitatively analyzing the impacts of climate change and human activities on runoff.
INTRODUCTION
River runoff is not only an integral component of hydrological cycles but also a key factor influencing changes in ecological and socio-economic development. However, the impacts of global warming on precipitation patterns and evaporation, along with human activities related to water resource development, will significantly alter the watershed's hydrological cycle processes and the spatial–temporal distribution of water resources, potentially even exacerbating the complexity of river hydrological characteristics. These changes will not only increase the frequency of drought and flood events but also impact the sustainable utilization of water resources and the healthy development of the watershed's ecological environment. In view of this, the attribution analysis of hydrological regimes has become a focal point of research in the field of water science (Zhang et al. 2020). Research on the impact of climate change and human activities on runoff variation not only facilitates a deeper understanding of the characteristics and dynamics of water resources but also provides a scientific basis for the rational development and utilization of water resources (Zhang et al. 2008).
The variation in runoff is an important condition for the economic development of the watershed. Both hydrological and meteorological indicators have an impact on runoff variation. According to the intergovernmental panel on climate change fifth assessment report, global surface temperatures have increased by approximately 0.85 °C from 1880 to 2012. Rising temperatures alter the evapotranspiration of basin vegetation, indirectly affecting water balance, increasing basin water-holding capacity, and consequently impacting the basin's hydro-environment (Sampson et al. 2021). Temperature rises may also trigger snow and ice melting, altering the seasonal distribution of runoff (Mimikou et al. 1999). Changes in precipitation directly affect the basin's water resources (Li et al. 2020). The increase in rainfall and its intensity may lead to increased river runoff and flooding probability during the flood season, while droughts may prolong during the non-flood season, further contributing to changes in hydrological conditions (Oeurng et al. 2019; Papalexiou & Montanari 2019; Krisnayanti et al. 2021). Previous scholars have conducted research on hydrology and meteorology. However, most current research tends to focus on independent analyses of watershed hydrological or meteorological conditions, establishing overall connections for response assessments such as runoff and precipitation, water temperature, and air temperature (Yang et al. 2024). There is a lack of exploration into the dynamic response relationships of potential detailed features of hydrological and meteorological changes. To address this gap, this study conducted a correlation analysis between hydrological conditions and meteorological indicators to provide more accurate references for water resource management.
Currently, methods for analyzing runoff changes primarily include neural network models (Govindaraju & Rao 2000), the elasticity coefficient method based on the Budyko hypothesis (Xiong et al. 2020), and hydrological modeling approaches (Xu & Jiang 2022). Among these, neural network models can utilize limited data to forecast runoff and demonstrate a certain level of feasibility. Anderson & Radić (2022) employed long short-term memory network models for predicting runoff in southwestern Canada. However, this model lacks a clear physical mechanism and is unable to distinguish the impacts of factors such as precipitation and evapotranspiration on runoff. The elasticity coefficient method based on the Budyko hypothesis offers clear physical significance, simplicity in application, and high feasibility, making it widely used in major river basins (Liu et al. 2019; Yan et al. 2020). Guo et al. (2022) quantitatively analyzed the contributions of climate change and human activities to runoff variations based on six Budyko hypothesis formulas. The results revealed that human activities were the dominant factor influencing runoff changes in the Min River Basin. However, this method can only separate and quantify the impacts of climate change and human activities on runoff changes at an annual scale, with limited applicability at smaller temporal and spatial scales. Hydrological modeling approaches not only possess robust physical mechanisms but also enable accurate simulation of runoff variability across multiple temporal scales, offering high analytical precision. Among these, the soil and water assessment tool (SWAT) stands out for its multifunctionality in simulating both climate change and anthropogenic impacts. Ji & Duan (2019) utilized the SWAT model to develop six simulation scenarios, providing an in-depth analysis of how land use/cover changes (LUCC) and climatic factors influence the hydrological cycle. Their study highlighted that the direct effects of meteorological conditions and human activities are the dominant forces behind the observed reductions in runoff. Therefore, considering the respective strengths and limitations of the Budyko and SWAT models, this study employs both the SWAT model and the elasticity coefficient method based on the Budyko hypothesis for comparative analysis of runoff change attribution, aiming to enhance the precision of the research. In addition, previous research predominantly evaluated the effects of climate change and human activities on runoff from a qualitative perspective or through single-scale quantitative analyses, often neglecting the spatial complexity exhibited by runoff (Xu et al. 2023). This also highlights the importance of quantifying the impacts of various driving factors on runoff across multiple temporal scales (monthly, seasonal, and annual) and spatial scales. Therefore, this study conducts attribution analysis at the subbasin scale based on hydrological simulations under different scenarios. It quantitatively identifies the impacts of climate change and human activities on runoff variations in the Jialing River (JLR) Basin from multiple temporal and spatial perspectives, providing scientific evidence for ecological protection and management planning in the JLR Basin.
As one of the major tributaries of the Yangtze River upstream, the JLR has abundant runoff and precipitation, making it among the tributaries with significant potential for the Yangtze River water resource development (Zhang et al. 2021). Therefore, studying hydrological changes in the JLR Basin and qualitatively and quantitatively assessing various influencing factors on runoff is of paramount importance for the ecological restoration and sustainable development of the Yangtze River Basin. The runoff variations in the JLR Basin are primarily influenced by climate change and human activities. Climate change manifests mainly in alterations in precipitation and temperature. Human intervention has resulted in a series of societal issues, such as extensive degradation of high-quality arable land, accelerated urbanization, severe damage to forests and grasslands, and further intensification of land development and utilization (Qi et al. 2013). These changes affect the water resource balance in the JLR Basin.
What is the correlation between hydrological and meteorological indicators in the JLR Basin? What are the primary driving forces behind the temporal and spatial variations in runoff? To address the aforementioned questions, this study comprehensively employs various methods to delve into the changes and dynamic responses of hydrological and meteorological indicators from different perspectives and makes a quantitative analysis of the hydrological regime, which mainly includes the following three steps: (1) temporal changes and identification of shift points in annual runoff from 1965 to 2019; (2) analysis of the characteristics and correlation of changes in hydrological and extreme meteorological indicators; (3) the Budyko and SWAT models are utilized to analyze the temporal and spatial variations of runoff as well as the primary driving factors causing these changes.
STUDY AREA AND DATA
Study area
Data preparation
The hydrological data collected from the Beibei Hydrological Station for the period 1965–2019 were sourced from the Yangtze River Commission. The Beibei hydrological station serves as a control station for the confluence of the JLR, Fu River, and Qu River, and it is also a downstream control station for the mainstream of the JLR. It can effectively reflect the hydrological situation of the JLR and is crucial for flood control in both the main urban area of Chongqing and the Three Gorges Reservoir area (Wang et al. 2019; Wu & Pu 2019).
Meteorological data were collected from ten meteorological stations in the JLR Basin for the period 1965–2019. These data include daily averages of temperature, daily high and low temperatures, rainfall, sunshine hours, and other variables. The data were sourced from the China Meteorological Information Center (http://data.cma.cn/).
Spatial data encompass soil, digital elevation model (DEM), and LUCC data. The DEM data were obtained from the China Academy of Sciences Resource and Environmental Sciences Data Center (http://www.resdc.cn/) at a resolution of 500 m × 500 m. Land use data and the normalized difference vegetation indicator (NDVI) data covering three periods (1980, 2000, and 2020) were also provided by the China Academy of Sciences Resource and Environmental Sciences Data Center, with a spatial resolution of 1 km × 1 km. Land use types were classified into six primary categories: arable land, forest land, grassland, water bodies, urban and rural construction land, and unused land. Soil data were derived from the Harmonized World Soil Database (http://www.fao.org/) at a scale of 1:1,000,000. Meteorological and hydrological data cover the period from 1965 to 2019.
METHODS
Detection of change points
This study employs the Mann–Kendall (MK) non-parametric test (Mann 1945; Kendall 1990) and the cumulative anomaly method (Chen et al. 2018) to analyze the change characteristics and identify change points in the annual mean runoff at the Beibei station. The MK is a non-parametric test method applied in the fields of hydrology and meteorology. This method has the advantages that the sample sequence does not have to follow a specific distribution, a small amount of missing data and abnormal data have little impact on the results, and can objectively show the overall change trend of the sample sequence. Therefore, it is widely used in trend analysis and change point detection in time series. Details of the calculation process can be found in the relevant literature (Chen et al. 2018; Zhuo et al. 2020).
Climate indicator analysis
The study selected 27 extreme climate indices jointly developed by the Expert Team on Climate Change Detection and Indices of the World Meteorological Organization. Based on daily data from 10 meteorological stations in the JLR Basin, calculations were performed using the RClimDex indicators developed by the Canadian Meteorological Center. Detailed information about these meteorological indices can be found on the website (http://etccdi.pacificclimate.org/list_27_indices.shtml). In this study, the icing days (ID) in the study are zero; therefore, ID is not considered in the meteorological analysis.
Hydrological situation analysis
In 1997, the indicators of the hydrologic alteration (IHA) method proposed by Richter et al. (1997) was widely utilized to assess the degree of hydrological changes in ecosystems under the influence of human activities. It encompasses five aspects: magnitude, timing, frequency, duration, and rate of change, totaling 33 hydrological indicators for the quantitative evaluation of hydrological alterations. In the Range of Variability Approach (RVA) analysis, calculations can be performed using either parametric (mean) or non-parametric (median) methods. Since parametric statistics require datasets to follow a normal distribution, whereas many hydrological datasets are skewed in reality, non-parametric statistics represent a better choice. Details of the IHA hydrological indicators are presented in Table 1.
Hydrological regime indicators and ecological impacts
Parameter group . | IHA Parameters . | Ecological influences . |
---|---|---|
Magnitude of monthly water level conditions | Monthly average streamflow (precipitation) | Provides habitat for aquatic organisms; maintains the soil moisture required for plant growth |
Magnitude and duration of annual extreme water level conditions | Annual average 1, 3, 7, 30, 90 days minimum and maximum streamflow (precipitation), base indicator | Maintains competitive balance between organisms; provides tectonic channels, geomorphology and physical habitat conditions |
Timing of annual extreme water level conditions | Date of occurrence of the maximum and minimum 1 day of the year (Roman day) | Access to specific habitats for breeding or to avoid predation; spawning opportunities for migrating fish |
Frequency and duration of high and low pulses | Average number of high and low pulses per year and the duration of the pulses | Exchange of nutrients and organic matter between the river and the floodplain; good for waterfowl feeding, roosting, nesting, and breeding |
Rate and frequency of water level condition changes | Annual average rates of increase and decrease and the number of reversals | Drought stress on plant production; drought stress on organic matter at the margins of low-mobility riverbeds |
Parameter group . | IHA Parameters . | Ecological influences . |
---|---|---|
Magnitude of monthly water level conditions | Monthly average streamflow (precipitation) | Provides habitat for aquatic organisms; maintains the soil moisture required for plant growth |
Magnitude and duration of annual extreme water level conditions | Annual average 1, 3, 7, 30, 90 days minimum and maximum streamflow (precipitation), base indicator | Maintains competitive balance between organisms; provides tectonic channels, geomorphology and physical habitat conditions |
Timing of annual extreme water level conditions | Date of occurrence of the maximum and minimum 1 day of the year (Roman day) | Access to specific habitats for breeding or to avoid predation; spawning opportunities for migrating fish |
Frequency and duration of high and low pulses | Average number of high and low pulses per year and the duration of the pulses | Exchange of nutrients and organic matter between the river and the floodplain; good for waterfowl feeding, roosting, nesting, and breeding |
Rate and frequency of water level condition changes | Annual average rates of increase and decrease and the number of reversals | Drought stress on plant production; drought stress on organic matter at the margins of low-mobility riverbeds |
To quantitatively analyze the degree of change in IHA indicators, criteria for hydrological alteration are established. Di values in the range of 0–33% are classified as low alteration; 33–67% as moderate alteration; and 67–100% as high alteration (Wang et al. 2023b).
Correlation analysis
Grey relational analysis
The grey theory proposed by Deng (1989) has been widely applied in various fields (Ip et al. 2009; Suh 2004) and is a simple yet accurate method for multi-attribute decision-making problems. The grey correlation analysis method, which involves non-dimensionalizing two sets of data through grey processing, as proposed by Lu & Wevers (2007), is a method for measuring the correlation between multiple factors by assessing the similarity or dissimilarity of the development trends between two sequences. Its objective is to identify the correlation between factors and determine the significant influence of certain factors. Therefore, by employing grey correlation analysis, it is possible to identify meteorological–hydrological factors with a high degree of correlation. For specific steps, refer to the literature by Liu et al. (2005).
Cross-wavelet analysis
In the equation, represents the complex conjugate of
,
denotes the cross product of the fluctuation amplitudes of two time series at a certain frequency, and
and
represent the amplitudes of the oscillating waves of the two time series.
Attribution analysis of runoff changes
Elasticity coefficient method based on the Budyko hypothesis


In the equation, ΔP, ΔET0, and Δn represent the changes in precipitation, potential evapotranspiration, and land surface characteristics before and after the shift, respectively. In this context, the sum of precipitation and potential evapotranspiration changes represents the variation in runoff induced by climate change, while the runoff change induced by land surface alterations signifies the impact of human activities on runoff.
The SWAT model
where ηC represents the contribution rate of climate change to runoff, ηH denotes the contribution rate of human activities to runoff, ηLUCC signifies the contribution rate of land use changes to runoff, and ηother indicates the contribution rate of other human activities to runoff.
ANALYSIS OF FINDINGS
Temporal characteristics of runoff and meteorological data
Interannual variations of precipitation, runoff depth, and air temperature from 1965 to 2019.
Interannual variations of precipitation, runoff depth, and air temperature from 1965 to 2019.
Seasonal variations in runoff depth and precipitation in the JLR Basin occurred during the base period and variation period.
Seasonal variations in runoff depth and precipitation in the JLR Basin occurred during the base period and variation period.
Temporal characteristics of meteorological and hydrological indicators during extreme events
Variations in meteorological indices characteristics
This study utilizes daily data from 10 meteorological stations along the JLR and employs the RClimDex method to compute various extreme climate indices.
Characteristics of hydrological indicator variation
This study utilizes daily runoff data from Beibei station for the period of 1965–2019 to analyze the runoff variations across the entire JLR Basin during the base period. The annual count of the 32 parameters of daily runoff falling within the target range during the change period is calculated to assess the degree of variation in runoff change parameters. Based on this, the IHA–RVA method is applied to calculate the degree of change in runoff during the base period and variation period at the Beibei hydrological station. The number of low pulses at Beibei station increased from 3 to 13, representing a change of 89.86%, which is a high probability. The duration of low pulses decreased from 11 to 2 days, representing a change of 55.38%. Meanwhile, the number of high pulses increased from 9 to 10, with a change of 22.78%, indicating a low change. The duration of high pulses decreased from 6 to 4 days, representing a change of 40.51%. The minimum flow for 1, 3, and 7 days all decreased to varying degrees, while the minimum flow for 30 and 90 days increased. The annual maximum flow after the change was lower than before the change, consistent with the results of the change-year test, indicating a decreasing trend in flow depth in the JLR. The reversal frequency significantly increased compared to before the change, with a change degree of 100%. Due to the limited resilience of ecosystems to external influences, the frequency of changes in JLR runoff will have a significant impact on the ecosystem of this basin.
Degree of hydrological change at Beibei station (Group 1 is count and duration time of extreme pulse; Group 2 is time of extreme flow occurring; Group 3 is extreme flow; Group 4 is monthly mid-value flow; Group 5 is reversals and rate of change).
Degree of hydrological change at Beibei station (Group 1 is count and duration time of extreme pulse; Group 2 is time of extreme flow occurring; Group 3 is extreme flow; Group 4 is monthly mid-value flow; Group 5 is reversals and rate of change).
Based on the overall hydrological indicator changes and the changes in various hydrological indices (Table 2), it can be concluded that the hydrological condition of the JLR Basin is most affected by the rate and frequency of water flow changes, with a degree of change of 77.34%. Following this are the frequency and duration of high and low pulses, as well as the occurrence time of annual extreme flows, all reaching a moderate level of change with degrees of change of 57.67 and 52.58%, respectively. The degree of change for the other two groups of indices is lower than that of the above three groups, and except for the fifth group of indices, which represents a high level of change, the rest belong to a moderate level of change. The overall degree of change in hydrological indices is 50.70%, indicating a moderate level of change.
Overall hydrological alteration of discharge sequence unit (%)
Hydrological . | Hydrological alteration degree of each group . | Overall hydrological alteration degree . | ||||
---|---|---|---|---|---|---|
Group 1 . | Group 2 . | Group 3 . | Group 4 . | Group 5 . | ||
Beibei | 57.67(M) | 52.58(M) | 36.13(M) | 41.77(M) | 77.34(H) | 50.70(M) |
Hydrological . | Hydrological alteration degree of each group . | Overall hydrological alteration degree . | ||||
---|---|---|---|---|---|---|
Group 1 . | Group 2 . | Group 3 . | Group 4 . | Group 5 . | ||
Beibei | 57.67(M) | 52.58(M) | 36.13(M) | 41.77(M) | 77.34(H) | 50.70(M) |
Note: H, high change; M, moderate change; L, low change.
Meteorological–hydrological correlation analysis
Significant impact characteristics of meteorological–hydrological events
Correlation between meteorological and hydrological changes during the study period.
Correlation between meteorological and hydrological changes during the study period.
The dynamic response of meteorological–hydrological interactions
To elucidate the dynamic response relationship between meteorological and hydrological factors and considering the heterogeneity in the propagation between meteorological and hydrological indicators, we conducted further analysis using cross-wavelet analysis on six pairs of meteorological–hydrological indicators showing relatively high correlation. These pairs include PRCPTOT, SDII, and 30-day minimum, high pulse count, and June.
At the annual scale, the WTC spectrum and cross-wavelet transform (XWT) of PRCPTOT, SDII and 30-day minimum, high pulse count, and June are depicted.
At the annual scale, the WTC spectrum and cross-wavelet transform (XWT) of PRCPTOT, SDII and 30-day minimum, high pulse count, and June are depicted.
Further analysis using the wavelet transform coherence (WTC) spectrum reveals that at a 95% confidence level, PRCPTOT and high pulse count, SDII and High pulse count, PRCPTOT and June, and SDII and June exhibit significant positive correlations primarily during the 4–6-month period from 2003 to 2006. Conversely, PRCPTOT and 30-day minimum and SDII and 30-day minimum show negative correlations at a 95% confidence level. Additionally, the distribution of short-duration and frequent occurrences in the low-frequency energy areas of the six indicators reflects their non-stationary relationship. Relatively, there is a statistically significant relationship within the wavelet cone of influence for all six indicators. The WTC spectrum indicates that throughout the entire period, meteorology and hydrology are indispensable driving forces for each other, demonstrating the existence of a stable correlation between the two.
Hydrological attribution analysis
Budyko hydrological regime change attribution
According to Table 3, during the change period in the JLR Basin, the p-value decreased by 2.14% compared to pre-change; ET0 increased by 3.32% compared to the baseline period; and the aridity indicator (ET0/P) also increased compared to the baseline period. The elasticity coefficients εp, εET0, and εn for the influencing factors on runoff after the change are 1.80, −0.80, and −0.77, respectively. This indicates that for a 1% increase in P, there is a 1.80% increase in runoff depth, a 0.80% decrease for ET0, and a 0.77% decrease for n. It is evident that the variation in JLR runoff is positively correlated with precipitation and negatively correlated with potential evapotranspiration. The change in runoff in the JLR Basin is most sensitive to precipitation and least sensitive to underlying surface conditions. Table 3 also presents the contribution rates of each driving factor to runoff. Compared to the baseline period, the multi-year average runoff during the variation period decreased by 53.14 mm. Precipitation decreased by 21.02 mm, resulting in a runoff decrease of 15.27 mm and a contribution rate of 27.49%. Potential evaporation increased by 30.75 mm, causing a runoff decrease of 9.67 mm with a contribution rate of 17.40%. The underlying surface parameters decreased by 0.13, leading to a runoff decrease of 30.62 mm and a contribution rate of 55.11%. Therefore, climate change-induced runoff changes amount to 24.94 mm, contributing 44.89%, and the underlying surface is the primary factor influencing runoff changes, contributing 55.114%. The sensitivity of runoff changes to underlying surfaces is minimal, while the contribution of underlying surfaces to runoff is predominant. Therefore, a detailed understanding of the impact of underlying surfaces on runoff is necessary.
Hydro-meteorological parameters in the JLR Basin
Parameters . | 1965–1993 . | 1994–2019 . | Contribution rate (%) . |
---|---|---|---|
R (mm) | 423.93 | 370.79 | — |
P (mm) | 984.10 | 963.08 | 27.49 |
ET0 (mm) | 926.07 | 956.82 | 17.40 |
n | 1.30 | 1.44 | 55.11 |
εp | 1.64 | 1.80 | — |
εET0 | −0.75 | −0.80 | — |
εn | −0.64 | −0.77 | — |
Parameters . | 1965–1993 . | 1994–2019 . | Contribution rate (%) . |
---|---|---|---|
R (mm) | 423.93 | 370.79 | — |
P (mm) | 984.10 | 963.08 | 27.49 |
ET0 (mm) | 926.07 | 956.82 | 17.40 |
n | 1.30 | 1.44 | 55.11 |
εp | 1.64 | 1.80 | — |
εET0 | −0.75 | −0.80 | — |
εn | −0.64 | −0.77 | — |
Calibration and validation of the SWAT model
In this study, the entire watershed was divided into 60 subbasins based on DEM data, and subbasin 59 outlet simulations were performed for measured flows at the Beibei station. Further subdivision into 470 HRUs was performed based on slope, LUCC types, and soil types within the 60 subbasins.
Sensitivity analysis of the SWAT model parameters and calibration results
Parameters . | Sensitivity ranking . | Final parameter values . |
---|---|---|
R_CN2.mgt | 1 | 0.1 |
V_ALPHA_BF.gw | 2 | 0.65 |
R_SOL_K.sol | 3 | 2.7 |
V_CANMX.hru | 4 | 65 |
V_CH_N2.rte | 5 | 0.135 |
V_ESCO.hru | 6 | 0.75 |
R_SOL_AWC.sol | 7 | 0.3 |
V_SFTMP.bsn | 8 | − 1 |
V_REVAPMN.gw | 9 | 209.450 |
Parameters . | Sensitivity ranking . | Final parameter values . |
---|---|---|
R_CN2.mgt | 1 | 0.1 |
V_ALPHA_BF.gw | 2 | 0.65 |
R_SOL_K.sol | 3 | 2.7 |
V_CANMX.hru | 4 | 65 |
V_CH_N2.rte | 5 | 0.135 |
V_ESCO.hru | 6 | 0.75 |
R_SOL_AWC.sol | 7 | 0.3 |
V_SFTMP.bsn | 8 | − 1 |
V_REVAPMN.gw | 9 | 209.450 |
Comparison of simulated and observed runoff at Beibei hydrological station before and after the change in 1965–2019.
Comparison of simulated and observed runoff at Beibei hydrological station before and after the change in 1965–2019.
SWAT hydrological regime change attribution
Quantifying the impacts and contributions of climate, land use, and other human activities to runoff changes at monthly, annual, and seasonal scales.
Quantifying the impacts and contributions of climate, land use, and other human activities to runoff changes at monthly, annual, and seasonal scales.
The spatiotemporal impacts of climate change and land use on runoff (where ‘a’ denotes runoff changes induced by climate variations and ‘b’ represents runoff changes induced by land use alterations).
The spatiotemporal impacts of climate change and land use on runoff (where ‘a’ denotes runoff changes induced by climate variations and ‘b’ represents runoff changes induced by land use alterations).
DISCUSSION
The impact of climate change and anthropogenic activities on runoff
This study quantitatively analyzes the impact of driving factors behind runoff variations on a spatiotemporal scale based on hydrological and meteorological indicators using the SWAT model. The results are compared with the annual-scale results obtained from the Budyko method. The results reveal that the two are broadly similar, with human activities consistently identified as the primary drivers of runoff variations, thereby affirming the reliability of the SWAT model. Building upon this foundation, quantitative attribution of runoff variations at seasonal and monthly scales was achieved using the model. Wang et al. (2023a), based on the Budyko hypothesis, concluded that human activities are the primary influencing factor, contributing 66.31% to the runoff in the mainstream of the JLR. Li et al. (2022) found that climate factors and human activities contributed 42.70 and 57.30%, respectively, to the variation in river discharge, while their contributions to vegetation change were 28.89 and 71.11%, respectively. Our findings not only corroborate their results at the annual scale but also extend the analysis to more detailed temporal scales. Furthermore, this study conducted a detailed quantitative analysis of spatial variations in runoff.
At the temporal scale, precipitation predominates among climatic factors, serving as a key driver of runoff variations (Shenglian et al. 2015). However, with increasing human activities, the relative influence of climatic factors on runoff diminishes, gradually making human activities the dominant factor in runoff variations (Hayashi et al. 2015). In spatial terms, the facilitating effect of land use on runoff is far less significant compared to the inhibitory effect of climate change on runoff. Additionally, there are other factors influencing the runoff variation in the JLR Basin, such as the construction and operation of reservoirs, as well as changes in groundwater levels within the basin. The construction of reservoirs is an important manifestation of human activity, which alters the natural runoff process of a river; when a reservoir is impounded, a large amount of water is stored in the reservoir and does not continue to flow along the river, leading to a reduction in the total amount of runoff from the river. In addition, in order to meet the needs for water for irrigated agriculture, economic development, and residential life, human beings have taken a series of measures to divert water resources away from rivers, which has led to a decrease in the runoff volume of rivers. Therefore, with the construction of cascade reservoirs creating an artificial steady environment devoid of natural extreme phenomena, this may be a contributing factor to the significant changes observed in hydrological indicators (Lu 2005).
Temporal and spatial changes in land use and vegetation cover from 1980 to 2020.
Temporal and spatial changes in land use and vegetation cover from 1980 to 2020.
The rate of change in land use and vegetation cover from 1980 to 2020.
The impact of runoff changes on ecology
The variation of runoff under natural conditions can serve as one of the crucial indicators for evaluating the level of ecological diversity and the vulnerability of the ecological environment in a watershed (Zhang et al. 2009). Runoff, as the overall manifestation of water flow within a watershed, is influenced by natural factors such as precipitation, evaporation, infiltration, and vegetation cover. Therefore, it can reflect the hydrological processes of the watershed and the state of the ecosystem, thereby assessing the adaptability of the ecosystem to hydrological changes. The majority of the extreme temperature indices in the JLR Basin exhibit an increasing trend, indicating a gradual warming of the basin. This trend may be due to global warming caused by strong anthropogenic emissions of greenhouse gases or changes in LUCC. The rise in temperature may lead to an increase in evapotranspiration, thereby affecting watershed runoff. In this study, it was observed that the runoff during the variability period was lower than that of the baseline period, and the hydrological alteration reached 50.70%, confirming this observation.
As a significant geographical and ecological region in the upper reaches of the Yangtze River, the JLR Basin boasts rich fish diversity, currently representing one-third of the entire Yangtze River Basin (Xu et al. 2012). The ecological environment protection and high-quality development of the basin have been elevated to a national strategic level. Due to the changes in the hydrological environment of the JLR Basin, fish, and vegetation within the basin have exhibited diverse responses. Furthermore, excessive alterations in water flow have modified the original aquatic ecosystem of rivers, affecting habitats suitable for fish growth and reproduction, thereby influencing fish migration patterns and reproductive behaviors (Liu et al. 2021). The reduction in fish habitat has led to alterations in the spawning times of major fish species and a sharp decline in spawning quantity, ultimately resulting in a decrease in the fish diversity indicator in the JLR (Yuan et al. 2019). With the increase in planktonic organisms in reservoirs, there is a likelihood of a corresponding increase in certain stagnant water fish species, which may contribute to stimulating the growth in reservoir fishery yield (Zeng et al. 2014). Taking the middle reaches of the JLR as an example, approximately 28 species of waterfowl have been observed, among which the species including the spot-billed duck, black-headed gull, and cormorant constitute 50.87% of the total bird population in the area (Jiang & He 2008). Recent surveys indicate a gradual decline in the number of waterfowl. This trend is primarily attributed to the ‘channelization’ of the JLR and the construction of cascade reservoirs, resulting in the submergence of large areas of riverine floodplains, central embankments, and other habitats suitable for waterfowl within the basin. The alteration has brought about noticeable changes to the surrounding ecological environment, and in the short term, the process of natural restoration to form new suitable habitats is relatively challenging (Zhou & Fang 1998). Meanwhile, the homogenization of water flow may reduce the water and nutrient availability necessary for the vegetation in the JLR Basin, resulting in a decrease in vegetation coverage (Xu 2019). Therefore, in studying and managing the hydrological environment of the basin, it is necessary to comprehensively consider the integrated impacts of human activities in order to develop effective water resource management strategies and environmental protection measures (Hu et al. 2016).
CONCLUSION
To investigate the impact of climate change and human activities on the runoff variation in the JLR Basin, this study employs the IHA–RVA method to assess hydrological conditions based on annual variability characteristics and quantifies the extent of runoff process changes in the altered environment. Concurrently, the RClimDex method is utilized to compute various extreme weather indices, providing deeper insights into the impact of climate change on runoff. Subsequently, grey relational analysis and cross-wavelet analysis are employed to analyze the dynamic response between meteorological and hydrological indicators. Finally, the SWAT model is utilized to quantitatively separate the impacts of climate change and human activities on runoff in both temporal and spatial dimensions, with validation conducted using the Budyko model based on the principle of water balance. This study provides scientific guidance for the spatial and temporal regulation of water resources in the JLR Basin through the quantification of meteorological and hydrological indicators and the attribution analysis on the spatial and temporal scales. The research findings indicate:
(1) The runoff at the Beibei hydrological station in the JLR Basin exhibited a decreasing trend from 1965 to 2019, and all passed the 95% significance test. Eventually, 1994 was determined to be the year of abrupt change. From 1965 to 2019, the JLR Basin experienced an increasing trend in extreme temperatures, with nighttime warming rates surpassing those of daytime, indicative of a warming climate. Apart from R10 mm, all other extreme precipitation indices showed an upward trend, suggesting a gradual drying trend in the JLR Basin. The hydrological variation degree in the JLR Basin was 50.70%, categorized as a moderate change.
(2) The correlation among the six sets of indices, including PRCPTOT and 30-day minimum, PRCPTOT and high pulse count, SDII and 30-day minimum, PRCPTOT and June, SDII – high pulse count, and SDII and June, is notably high. PRCPTOT and High pulse count, PRCPTOT and 30-day minimum, SDII and high pulse count, and SDII and 30-day minimum exhibit significant resonance periods during 1965–2019, mainly concentrated in the 4–6-month signals from 2000 to 2006.
(3) Utilizing the Budyko framework for quantitative attribution, it was found that precipitation is the most sensitive driver of runoff variability in the JLR Basin. At the annual and seasonal scales, human activities emerge as significant influencing factors on runoff variations in the JLR. However, at the monthly scale, there is a notable shift in the proportion of different drivers, with climate change exhibiting the highest contribution in March at 77.03%, except for March and November, the remaining months are dominated by human activities. In spatial terms, the impact of climate change on runoff outweighs that of land use and is predominantly concentrated in the southeastern region.
ACKNOWLEDGEMENTS
The authors thank their brothers at North China University of Water Resources and Electric Power for their comments and help with this study.
AUTHOR CONTRIBUTIONS
H. W. rendered support in funding acquisition project administration, and prepared the resources. Y. M. conceptualized the process, rendered support in data curation and formal analysis, investigated the study, developed the methodology, prepared the resources and software, validated and visualized the work, wrote the original draft, wrote the review and edited the article. W. Y. investigated the process, rendered support in formal analysis, developed the methodology, validated the work, prepared the resources. H. Y. rendered support in data curation and formal analysis, investigated and validated the work. H. Y. and Y. L. investigated the work, rendered support in formal analysis, validated the study, prepared the resources. W. G. rendered support in funding acquisition and project administration.
FUNDING
This study was supported by the Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (24ZX007).
DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.
CONFLICT OF INTEREST
The authors declare there is no conflict.