Quantitatively separating the influence of climate change and human activities on runoff is crucial to achieving sustainable water resource management in watersheds. This study presents a framework for quantitative assessment by integrating the indicators of hydrologic alteration, the whale optimization algorithm and random forest (WOA-RF), and the water erosion prediction (WEP-L) models. This framework quantifies the differences in hydrological conditions and their driving forces at multi-timescales (annual, seasonal, and monthly). The results indicate that the runoff of the Wu River has decreased since 2005. Climate factors were found to influence the interannual variation of runoff mainly. Meanwhile, human activities had a more significant impact in autumn, with a relative contribution rate of 59.0% (WOA-RF model) and 70.8% (WEP-L model). Monthly, the picture is more complex, with the results of the WOA-RF model indicating that climate change has a significant impact in July, August, and September (88.8%, 92.7%, and 79.3%, respectively). However, the WEP-L model results showed that the relative contribution of land use is significant in April, May, June, October, and November (51.24%, 64.23%, 63.63%, 53.16%, and 50.63%, respectively). The results of the study can be helpful for regional water allocation.

  • Based on indicators of the hydrologic alteration range of variation approach, the changes of hydrological regime in the Wujiang River basin in the past 30 years were analyzed.

  • Natural runoff was reconstructed by the whale optimization algorithm and random forest and the water erosion prediction models.

  • The effects of climate change and human activities on runoff change on annual, monthly, and seasonal scales were quantified.

Climate change and human activities have altered global hydrological-cycle processes (Dimitriadis et al. 2021). Climate change directly affects the hydrological cycle through the amount of precipitation or evapotranspiration (Remondi et al. 2015). Warmer temperatures will accelerate surface evaporation processes, releasing more vapors into the air and possibly increasing the probability of extreme events (Lau & Kim 2017). Human activities affect runoff by regulating reservoirs and altering water cycle patterns, with land use and land cover change being one of the most direct ways (Yang et al. 2019). As the climate warms and human activities continue to intensify, many rivers have experienced significant decreasing trends in runoff (Wang et al. 2012). These changes threaten global or regional water security.

The driving mechanisms influencing changes in runoff vary considerably across timescales. It is essential to quantify the impact of climate change and human activities on hydrological conditions at different timescales based on different models (Yuan et al. 2016). As the characteristics of runoff changes are important indicators for maintaining the balance between the supply and demand and the ecological stability in the basin, it is of practical importance to analyze the hydrological changes in the basin and their driving mechanisms (Song et al. 2021).

Many scholars have analyzed the hydrological condition qualitatively by analyzing the hydrological changes in the basin (Shahid et al. 2021). Previous studies have focused on three aspects to analyze runoff-driving mechanisms. One is the quantitative attribution of runoff changes based on different analytical formulations of the Budyko hypothesis. For example, Hou et al.’s (2022) attribution analysis of runoff changes in river basins along the Loess Plateau based on the elasticity coefficient method of the Budyko hypothesis found that the contribution of changes in substrate conditions to runoff reached 96.78%. Zheng et al. (2021) found that, based on the Budyko method for attributing runoff changes in the Kuye River basin, catchment surface-characteristic parameters were the most critical factors influencing runoff changes. However, the Budyko model could only quantify the contribution of runoff change drivers on an annual scale and could not quantitatively separate the effects of runoff change drivers on different timescales. The second aspect is the reconstruction of natural runoff based on the data-driven model, and then the causes of runoff changes being analyzed based on the differences between simulated and actual runoff. Fan et al. (2021) analyzed the runoff-driving mechanism of the Poyang Lake basin based on the long short-term memory model (LSTM) and found that the runoff changes were mainly influenced by climate change, with an average influence weight of 75.84%. Wang et al. (2023) proposed a monthly runoff interval prediction method based on whale optimization algorithm (WOA)-VMD-LSTM and nonparametric kernel density estimation, where VMD is variational mode decomposition. Dodangeh et al. (2021) proposed a new data intelligence model developed by mixing the adaptive neuro-fuzzy inference system with gray wolf optimizer, particle swarm optimizer, and WOA for the prediction of the daily river flow of the Talhan River, which is the main source of drinking water for Tehran, the capital of Iran. The success of the WOA model demonstrates its potential to be used for modeling highly nonlinear daily rainfall–runoff relationships. It indicates that the WOA-algorithm-optimized machine learning model coupled with hydrological modeling for runoff prediction has higher accuracy on daily and monthly scales and is more suitable for the analysis of drivers of runoff changes (Liu et al. 2022).

The third aspect is the restoration of the natural runoff series by physical hydrological models, including the soil and water assessment tool (SWAT) model and variable infiltration capacity (VIC) model. Measured and simulated runoffs were compared to quantify the weight of climate change and human activities on the driving mechanisms of runoff change. Chen et al. (2022) reconstructed the unaffected natural runoff series in the river basin by the SWAT model and found human activities to be the primary driver of runoff change by calculation. Yang et al. (2020) used the VIC model to analyze runoff variability in the Wei River basin and found that climate change was the leading cause of runoff reduction.

The principles and data collection methods for natural runoff restoration vary among different models, resulting in limitations and relativity in the attribution analysis of runoff changes. Therefore, more previous research that analyzes the driving factors of runoff changes in a watershed based on a single model at the annual scale is required. In addition, the current research needs more analysis at more detailed timescales (seasonal and monthly). It fails to consider multiple factors such as the seasonal distribution of precipitation and the operation of reservoirs, which are essential drivers of changes in runoff processes (Ning et al. 2022). In summary, this study aims to (1) use a whale-optimization-algorithm-optimized random forest model and a water erosion prediction (WEP-L) model to simulate and restore natural runoff at the annual, monthly, and seasonal scales; (2) analyze the ecological response of runoff using the indicator of hydrologic alteration (IHA) range of variation approach (RVA) method; and (3) employ a new framework that integrates models of different types to comprehensively analyze and quantify the relative contributions of climate change, human activities, and land use at multiple levels and scales, as well as considering the absolute changes at the monthly scale.

Study area

The Wu River is the largest southern tributary in the upper reaches of the Yangtze River, featuring a feather-like distribution. High elevations characterize the terrain in the southwest, while the northeast consists mainly of plateaus and hills. Its primary functions are power generation, flood control, and navigation (Guo et al. 2021b). The Wu River spans between 26°N to 30°N latitude and 104°E to 110°E longitude, covering a watershed area of 4.82 × 1010 m3. With a main channel length of 1,037 km and a natural drop of 2,124 m, its source is located near Bijie city in the upper region and it flows downstream to the Pingshui hydropower station. The Wulong hydrological station sits in the lower reaches of the Wu River basin, approximately 60 km downstream from its confluence with the Fuling River and entry into the Yangtze River. Being the largest right-bank tributary in the upper reaches of the Yangtze River, the Wu River constitutes one of the 13 main hydropower bases in China (Figure 1). In recent years, there has been a downward trend in runoff within the Wu River basin (Wu et al. 2018).
Figure 1

Overview of the upper reach of the Wujiang River basin.

Figure 1

Overview of the upper reach of the Wujiang River basin.

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Data sources

As can be seen from Table 1, in this study, we employed daily flow data from the Wulong hydrological station between 1990 and 2019, and daily meteorological information was obtained from 14 different meteorological stations, in addition to land-use data spanning six periods (1990, 1995, 2000, 2005, 2010, and 2015) and digital elevation model (DEM) data. The daily flow data were acquired from the Yangtze River Water Resources Commission (http://www.cjw.gov.cn/), meteorological information was obtained from the National Meteorological Science Data Center (https://data.cma.cn/), land-use data were sourced from the Environmental and Resource Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn), and the DEM data were obtained from HydroSHEDS (https://www.hydrosheds.org).

Because of the progressively complicated environment of watersheds and the extensive development of large-scale water conservancy projects, the influence of human activities on runoff changes has grown considerably. Single models and scales are inadequate for comprehensively comprehending the underlying reasons for runoff variations and do not cater to the requirements of local watershed managers. Consequently, this study established a multimodel and multi-scale framework for quantitative attribution analysis of watershed runoff. The fundamental process framework is illustrated in Figure 2.
Figure 2

Research framework of the fundamental process.

Figure 2

Research framework of the fundamental process.

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Hydrological mutation test

The Mann–Kendall test is a nonparametric statistical method with the advantage of not requiring adherence to a specific distribution and being robust to outliers. Furthermore, it can identify multiple change points and is characterized by its ease of computation. The Mann–Kendall test has been extensively utilized in trend analysis and change point detection of runoff (Guo et al. 2022). The Mann–Kendall (M−K) test is a nonparametric statistical test method widely used in hydrometeorological time series (Phuong et al. 2020). In the M–K trend test, a positive statistic indicates an increasing trend, otherwise a decreasing trend. Statistical variables are defined as follows:
(1)
where SK is the cumulative number of sample symbols, E(SK) is the sample mean, and Var(SK) is the sample variance. Variable UBK is calculated according to the sequence's reverse time sequence, and the curves formed by the two statistical sequences are recorded as UF and UB.

The cumulative anomaly method is mainly applied in hydrometeorological research. It verifies long-term trend changes in a sequence by assessing the degree of dispersion of the accumulation anomaly value Xt relative to the mean of the data. In the event of Xt decreasing, it signifies a value lower than the mean of the data, while its increase represents a value higher than the mean. The fluctuation chart of its long-term series can show the growth and decline of the data. Typically, the cumulative anomaly plot exhibits distinctive sections, where Xt increases or decreases, and the turning points where they occur reflect the inflection points of the data trend.

The cumulative anomaly value of a specific time t in a time series x(x1, x2, x3,…xn) can be expressed as follows:
(2)

Indicators of hydrologic alteration

To quantify the variability of hydrologic conditions in rivers, the RVA is based on the hydrologic alteration index (IHA) proposed by Yin et al. (2015). Richter et al. (1996) adopted the RVA method to evaluate the hydrological regimes of rivers impacted by external factors. As shown in Table 2, it is divided into five categories: magnitude, timing, duration, frequency, and variability.

Furthermore, the standard deviation of the mean value of each index or the probability of occurrence of a particular index was used as the upper and lower limits of the index, which are the target boundaries of RVA. The degree to which a river is impacted was evaluated by calculating the IHA index values before and after any hydrologic alteration points. Richter et al. (1996) proposed the RVA method to quantitatively assess the degree of impact on hydrological indicators before and after the mutation year, with the specific calculation formula being:
(3)

Noi and Ne are the actual and desired numbers of years that fall within the RVA target threshold after the hydrological indicator alteration. To determine an objective standard for the degree of change in IHA indicators, it was established that values of Di between 0% and 33% indicated low alteration. In comparison, values between 33% and 67% were considered a medium alteration, and values between 67% and 100% indicated high alteration. The overall degree of hydrologic alteration can be evaluated by averaging the hydrologic alterations of the 33 IHAs, but this does not reflect the weightings of individual indicators. More significant weightings were assigned to larger Di values to reflect each indicator's weight.

The formula for calculating the change degree is as follows:
(4)

Among them, a Do value of less than 33% is considered low alteration, between 33% and 67% is considered medium alteration, and 67%–100% is considered high alteration (Zhang et al. 2019).

Random forest model with whale optimization algorithm

The random forest model represents a contemporary classification and regression technique of ensemble learning methods. The bootstrapped sample sets for this model are established through the random selection of variables and attributes. The classification regression decision tree is an ensemble learning model of individual learning machines within this model. Nevertheless, a challenge to effectively reshaping natural runoff with this model has been observed, which stems from an incomplete collection of driving factors and relatively significant simulation errors. In this context, the present study presents a solution in the form of a random forest model coupled with whale optimization algorithm, which enables a more accurate reconstruction of natural runoff. The whale optimization algorithm initializes a group of individuals in the feasible solution space. Each whale represents a potential optimal solution for extreme value optimization problems, and its position represents its characteristics. The fitness function calculates the fitness value. The quality of fitness represents the superiority or inferiority of the whale. The whales explore the solution space and update their positions by simulating the behaviors of enclosing prey, hunting, and searching for a game (Lv et al. 2020; Wang et al. 2023). Every time the whale population updates its position, it calculates the fitness value and compares it to the current optimal fitness value (Long et al. 2020). The input factors of the whale optimization algorithm and random forest (WOA-RF) model in this study include precipitation, air temperature, potential evapotranspiration, solar radiation, wind speed, and relative humidity, and the output factor is monthly runoff. The monthly mean flow before the mutation and the monthly mean rainfall, air temperature, potential evapotranspiration, and solar radiation after the mutation are used to restore the monthly mean natural runoff from 1990 to 2019 under the influence of only climate change based on the optimization of manual parameters.

The water erosion prediction model

The WEP-L model is a distributed hydrological model based on the water cycle and surface energy exchange processes. It uses contour strips within the basin as the calculation unit and is suitable for fitting larger watersheds. This ensures the water balance of the calculation unit and the flow paths' non-distortion and improves the model's computational efficiency. Therefore, it is suitable for large-scale hydrological simulations. As shown in Figure 3, the WEP-L model chooses the ‘subbasin nested contour band’ as the basic unit for its planar structure. In the case of the unique hydrological cycle characteristics of the Yellow River basin, a super-large watershed, the basin is divided into subbasins with topological relationships. Then each subbasin is subdivided into multiple contour bands based on its elevation, which serves as the basic unit for water cycle simulation. The subbasins are obtained by dividing the river network into smaller systems, with each river channel and surrounding area forming a subbasin. The subdivision of contour bands in mountainous subbasins mainly considers the catchment runoff processes on slopes and the impact of elevation on the water cycle in plateau and mountainous regions. This subdivision method can ensure the accuracy of factors such as runoff path and water balance within each unit and is convenient for simulating large watersheds. In this study, the Wu River basin was divided into 765 subbasins and 8,943 contour bands, and a natural circulation simulation of the 30-year runoff of the Wu River was conducted (Xu et al. 2021). In this study, the Wujiang River basin was divided into 765 subbasins and 8,943 isobath zones through the model based on the land-use data and DEM elevation data for 1990, 1995, and 2000, as well as the monthly mean temperature, rainfall, potential evapotranspiration, solar radiation, wind speed, and relative humidity data for the period of 1990–2019 as the input. By manually adjusting the model's parameters, the 30-year monthly mean runoff of the Wujiang River basin under the influence of climate change was restored from 1990 to 2019.
Figure 3

Hydrological model of horizontal structure – subbasin set and other high band basic units.

Figure 3

Hydrological model of horizontal structure – subbasin set and other high band basic units.

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Model assessment

The simulation effect of the model is mainly determined by the Nash–Sutcliffe efficiency (NSE), root-mean-square error (RMSE), and correlation coefficient (R2), in which NSE and R2 are close to 1 and the model simulation effect is better. At the same time, the closer the RMSE value is to 0, the better is the simulation effect.
(5)
(6)
(7)
where Qsim,i and Qobs,i represent the monthly simulated runoff and station observation results (m3/s), respectively; N represents the number of months; and Qobs,i represents the monthly measured annual mean. Qi is the average monthly runoff for a particular month, is the fitting simulation value for the monthly runoff, Q represents the mean monthly runoff, and n represents the number of months (Xu et al. 2022).

Attribution of runoff changes

This study adopts the computational relative contribution model proposed by Peng et al. (2016) to assess the absolute and relative contributions of human activities and climate change to the variation of runoff:
(8)
(9)
(10)
Qobs1 and Qobs2 represent the measured monthly average runoff (m3/s) for the natural and variable periods, respectively, while Qsim1 and Qsim2 denote the simulated monthly average runoff (m3/s) for the same periods.
(11)
(12)
where ηh and ηc represent the relative contribution rates of human activities and climate change, respectively, to the runoff variation.

Analysis of hydrological conditions at multiple timescales

The analysis of mutation tendency in the annual average runoff sequence of Wulong station from 1990 to 2019 using the M–K test and the cumulative anomaly method shows that the mutation year is 2005 (Table 3). As shown in Figure 4(a), the annual average runoff at Wulong station fluctuates wildly and offers an overall downward trend. The magnitude of the decrease is approximately 302.89 m3/s. After conducting the M–K trend test, the annual average runoff at the Wulong station exhibits a significant decreasing trend that passes through the significance level (95%), indicating that the downward trend is significant. Through the analysis of the monthly median runoff and runoff difference after the mutation (Figure 4(b) and 4(c)), it was found that the median runoff at the Wulong station decreases to varying degrees after the mutation, with prominent decreases in May, June, July, and August, and especially in June and July, where the reduction in median runoff reaches 550 and 1,220 m3/s, respectively. In the remaining months, the decrease in runoff is not prominent, and the change after the mutation is insignificant. Overall, the decline in summer runoff is more significant than in spring, autumn, and winter after the conversion, as summer is the flood season and reservoirs are filled up. Since 2005, many large-scale pools have been constructed in the Wu River basin, which has led to a significant increase in summer water storage in reservoirs (Tian et al. 2022), resulting in a severe decrease in monthly median runoff during the flood season. Climate change is the main cause of runoff changes. Human activities, such as the construction of large hydropower projects, have increased water storage during the flood season, greatly exacerbating these changes (Su et al. 2020).
Table 1

Description of the hydrological and meteorological stations in the study area

StationLongitude (E)Latitude (N)Altitude (m)
Hydrological station    
Wulong 107°45′ 29°19′ 277.9 
Meteorological stations    
Weining 104°15′ 26°51′ 2,234.5 
Shuicheng 104°47′ 26°44′ 1,811.7 
Qianjiang 108°47′ 29°31′ 580 
Zhengan 107°21′ 28°43′ 679.7 
Youyang 108°46′ 28°48′ 663.7 
Bijie 105°14′ 27°18′ 1,510.6 
Xifeng 106°43′ 27°2′ 1,115.1 
Meitan 107°29′ 27°47′ 791.8 
Yuqing 107°44′ 27°5′ 600 
Sinan 108°13′ 27°56′ 420.6 
Qianxi 106° 27°3′ 1,216.6 
Zhijin 105°42′ 26°39′ 1,319 
Anshun 105°55′ 26°14′ 1,392.9 
Guiyang 106°42′ 26°34′ 1,071.2 
StationLongitude (E)Latitude (N)Altitude (m)
Hydrological station    
Wulong 107°45′ 29°19′ 277.9 
Meteorological stations    
Weining 104°15′ 26°51′ 2,234.5 
Shuicheng 104°47′ 26°44′ 1,811.7 
Qianjiang 108°47′ 29°31′ 580 
Zhengan 107°21′ 28°43′ 679.7 
Youyang 108°46′ 28°48′ 663.7 
Bijie 105°14′ 27°18′ 1,510.6 
Xifeng 106°43′ 27°2′ 1,115.1 
Meitan 107°29′ 27°47′ 791.8 
Yuqing 107°44′ 27°5′ 600 
Sinan 108°13′ 27°56′ 420.6 
Qianxi 106° 27°3′ 1,216.6 
Zhijin 105°42′ 26°39′ 1,319 
Anshun 105°55′ 26°14′ 1,392.9 
Guiyang 106°42′ 26°34′ 1,071.2 
Table 2

IHA parameter grouping and a brief description

IHA statistics groupCharacteristicsParameters
Monthly water conditions (1–12) Magnitude, timing The mean value for each calendar month 
Extreme water conditions (13–23) Magnitude, duration Annual 1, 3, 7, 30, 90 d minimum and maximum; Base flow index 
Timing of extreme water conditions (24–25) Timing Julian date of each annual 1 d minimum and maximum 
High and low pulses (26–29) Magnitude, duration, frequency No. and duration of low/high pulses 
Water condition changes (30–32) Frequency, variability No. of rises, falls, and reversals 
IHA statistics groupCharacteristicsParameters
Monthly water conditions (1–12) Magnitude, timing The mean value for each calendar month 
Extreme water conditions (13–23) Magnitude, duration Annual 1, 3, 7, 30, 90 d minimum and maximum; Base flow index 
Timing of extreme water conditions (24–25) Timing Julian date of each annual 1 d minimum and maximum 
High and low pulses (26–29) Magnitude, duration, frequency No. and duration of low/high pulses 
Water condition changes (30–32) Frequency, variability No. of rises, falls, and reversals 
Table 3

Statistics for years with sudden changes in annual average flows

Hydrological stationYear of mutationMutation point
 M–K test Cumulative distance leveling method 2005 
Wulong station 1991 2005 2015 2017 2005 1995 
Hydrological stationYear of mutationMutation point
 M–K test Cumulative distance leveling method 2005 
Wulong station 1991 2005 2015 2017 2005 1995 
Figure 4

Characteristics of runoff evolution at different timescales: (a) graph of annual mean flow trends, (b) graph of monthly median flows before and after abrupt changes, and (c) graph of monthly median flow differences.

Figure 4

Characteristics of runoff evolution at different timescales: (a) graph of annual mean flow trends, (b) graph of monthly median flows before and after abrupt changes, and (c) graph of monthly median flow differences.

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Quantitative analysis of hydrological conditions in a changing environment

To quantitatively evaluate the hydrological changes in the watershed before and after the mutation year, the daily runoff data from the Wulong station was divided into two periods, with the year of mutation, 2005, serving as the dividing point: (1) before 2005, the hydrological process at the Wulong station was considered as a baseline runoff sequence under natural conditions; (2) between 2005 and 2019, the daily runoff process was regarded as a time series influenced by human activities, and the threshold for hydrological objectives was determined using the interval range of the mean parameter value plus or minus the standard deviation in the water level sequence under natural conditions. Thus, the median annual runoff changes and the upper and lower limits of the RVA objective in the upstream area of the Wulong station were obtained for both periods. Based on this calculation method, the IHA and RVA methods were comprehensively applied to analyze the degree of hydrological changes in the Wu River basin before and after a hydrological mutation event (Figure 5). Among the 32 hydrological indicators, high-change indicators accounted for 22%, moderate-change indicators accounted for 50%, and low-change indicators accounted for 28%. The hydrological indicator most affected by human activities in terms of changes in runoff degree was the declining rate, with a change degree reaching 83.33%. Most hydrological indicators in the Wu River basin showed a moderate degree of change before and after the flow mutation, including the average monthly runoff in January, February, April, May, and July (40%), the average monthly runoff in June and December (60%), the 90-day minimum runoff (40%), the yearly one-day maximum runoff (40%), the changes in annual maximum seven-day, 30-day, and 90-day runoff, which were also the same (40%), the minimum number of days (40%), the low pulse count (66.67%), and the high pulse count (42.86%). Table 4 shows that, except for the third group with low alteration, the Wulong hydrological station had moderate change for all indicators. The alteration degree of the fifth group was 64%, which is close to high alteration. The overall change index had a degree of change of 46.17%, considered moderate.
Table 4

Overall hydrological variability

Hydrological stationDegree of change for each group
Overall hydrological variability
Group 1Group 2Group 3Group 4Group 5
Wulong station 50% (M) 38% (M) 32% (L) 44% (M) 64% (M) 46% (M) 
Hydrological stationDegree of change for each group
Overall hydrological variability
Group 1Group 2Group 3Group 4Group 5
Wulong station 50% (M) 38% (M) 32% (L) 44% (M) 64% (M) 46% (M) 
Figure 5

IHA-RVA 32 indicators of the degree of change.

Figure 5

IHA-RVA 32 indicators of the degree of change.

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Model parameterization and evaluation results

WOA-RF model parameterization and evaluation

As is shown in Table 5, the relevant parameters of the random forest model optimized by the whale algorithm mainly include population size (POP), maximum iteration times (Max-iter), number of hyperparameters (dim), lower bound (lb), and upper bound (ub). The values used in this study were 10, 30, 2, [1,1], and [10,10], respectively. It can be seen from Table 6 and Figure 6 that the WOA-RF model divided the data into four periods, and the simulation effect was above 0.8 for all periods, indicating that the model had an excellent simulation effect. The calibration and verification periods were grouped, while the base and simulation periods were grouped. The NSE and R2 of the calibration period were 0.96 and 0.97, respectively, indicating that the model had high learning accuracy. The NSE and R2 of the verification period were 0.84 and 0.85, respectively, indicating that the model's simulation effect was as expected. The good verification effect based on the calibration period proved that the simulation effect of the model based on the base period was reliable, and the natural runoff restored by the model could be used as a basis for analyzing the changes in runoff in the Wu River basin.
Table 5

WOA-RF parameter rate specifies the value

WOA-RF parameterPOPMax_iterdimlbub
Parameter value 10 30 [1,1] [10,10] 
WOA-RF parameterPOPMax_iterdimlbub
Parameter value 10 30 [1,1] [10,10] 
Table 6

Statistics on the effect of natural runoff modeling in the WOA-RF Wujiang River basin

Hydrological stationRegular rate 1990–1997Validation period 1997–2004Base period 1990–2004Simulation period 2005–2019
Wulong station NSE R2 NSE R2 NSE R2 NSE R2 
 0.96 0.97 0.84 0.85 0.88 0.89 0.83 0.815 
Hydrological stationRegular rate 1990–1997Validation period 1997–2004Base period 1990–2004Simulation period 2005–2019
Wulong station NSE R2 NSE R2 NSE R2 NSE R2 
 0.96 0.97 0.84 0.85 0.88 0.89 0.83 0.815 
Figure 6

Model reliability validation period simulations: (a) line chart for the entire simulation period; (c) base period simulation line chart; (e) regular rate simulation line chart; (g) verification period simulation line chart; (b) correlation graph of actual and simulated traffic during the whole simulation period; (d) correlation chart of actual and simulated traffic during the base period; (f) regular rate actual and simulated flow correlation charts; and (h) correlation graph of actual and simulated traffic during the validation period.

Figure 6

Model reliability validation period simulations: (a) line chart for the entire simulation period; (c) base period simulation line chart; (e) regular rate simulation line chart; (g) verification period simulation line chart; (b) correlation graph of actual and simulated traffic during the whole simulation period; (d) correlation chart of actual and simulated traffic during the base period; (f) regular rate actual and simulated flow correlation charts; and (h) correlation graph of actual and simulated traffic during the validation period.

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WEP-L hydrological model rate determination and evaluation

This study developed a distributed hydrological model based on land use and meteorological data in the Wu River basin. Unlike data-driven models, distributed hydrological models provide a more comprehensive and accurate representation of the study area (Martin et al. 2017). To ensure the accuracy of natural runoff simulation, the WEP-L model employed a manual calibration approach for parameter tuning during the restoration process (Xu et al. 2021). The parameters of the WEP-L model mainly include basin storage capacity-related parameters, evaporation-related parameters, water-movement-related parameters, and surface interception-related parameters. The aquifer thickness correction coefficient specifically determines the basin storage capacity-related parameters. The aquifer thickness is the thickness between the ground and the impermeable layer. The suggested range for the aquifer thickness correction coefficient is between 0.1 and 20, and this study used a value of 2.5.

The evapotranspiration parameters mainly affect soil evaporation, vegetation transpiration, and runoff. These parameters include the thicknesses of the first three soil layers and the stomatal resistance correction coefficient. The recommended ranges for these values are as follows: 0.1–0.8, 0.2–2, 0.3–4, and 0–100, and in this study, the values were set to 0.2, 1, 1, and 100. The water-movement-related coefficients include the riverbed bottom material's hydraulic conductivity correction coefficient and the aquifer's lateral hydraulic conductivity correction coefficient. The suggested range for these values is 0.01–100, and in this study, the values were set to 50 and 3, respectively. Regarding surface-interception-related parameters, the main factor is the selection of the intercepted runoff storage capacity in the depression. The recommended values for different land-use types are as follows: forestland: 20 mm, grassland: 12 mm, bare land: 2 mm, sloping cultivated land: 5 mm, paddy fields: 60 mm, irrigated farmland: 50 mm, non-irrigated farmland: 50 mm, dam sites: 50 mm, and terraced fields: 80 mm.

Table 7 and Figure 7 show that the overall NSE of simulated results at different periods is above 0.74, the overall correlation coefficient is above 0.9, and the absolute relative error is about 8.79. The simulation was based on meteorological and land-use data from 1900 to 2019, with different periods selected as the base and influence periods. The contrast period is simulated using the input of meteorological data from the influence period and land-use data from the base period for natural runoff simulation. The simulation results of the natural runoff at the Wulong station under four different scenarios using the same parameters are good, and the monthly average runoff of the simulated runoff can serve as the basis for attributing changes in flow.
Table 7

Statistics of the simulation effect indicators of natural runoff of the Wujiang River under different scenarios of the WEP-L model

Hydrological stationsOverall simulation 1990–2019Base period 1990–2004Impact period 2005–2019Control period 2005–2019
Wulong Station NSE RMSE R2 NSE RMSE R2 NSE RMSE R2 NSE RMSE R2 
 0.82 −9.55 0.92 0.9 −13.54 0.96 0.63 −4.73 0.86 0.7 7.35 0.85 
Hydrological stationsOverall simulation 1990–2019Base period 1990–2004Impact period 2005–2019Control period 2005–2019
Wulong Station NSE RMSE R2 NSE RMSE R2 NSE RMSE R2 NSE RMSE R2 
 0.82 −9.55 0.92 0.9 −13.54 0.96 0.63 −4.73 0.86 0.7 7.35 0.85 
Figure 7

Model reliability validation period simulations: (a) line chart for the entire simulation period; (c) base period simulation line chart; (e) line chart in the simulation period; (g) line chart in the control period; (b) correlation graph of actual and simulated traffic during the simulation period; (d) correlation chart of actual and simulated traffic during the base period; (f) correlation diagram of actual and simulated traffic flow during the simulation period; and (h) correlation chart of actual and simulated flow during the control period.

Figure 7

Model reliability validation period simulations: (a) line chart for the entire simulation period; (c) base period simulation line chart; (e) line chart in the simulation period; (g) line chart in the control period; (b) correlation graph of actual and simulated traffic during the simulation period; (d) correlation chart of actual and simulated traffic during the base period; (f) correlation diagram of actual and simulated traffic flow during the simulation period; and (h) correlation chart of actual and simulated flow during the control period.

Close modal

Comparison of simulation results between the WOA-RF and WEP-L models

Figure 8 shows that the data-driven model has a higher reasonable degree in the early stage than the distributed hydrological model. However, because only meteorological factors are collected, the simulated runoff during the simulation period is higher than the measured runoff and the runoff simulated by the WEP-L model. The distributed hydrological model, on the other hand, has multiple input factors. Therefore, the simulated runoff in the later stage is closer to the actual value and lower than the runoff simulated by the WOA-RF model. This indirectly reflects that different models with different operating principles and data selection may lead to varying results in reconstructing natural runoff. Simulation results are better than the data-driven models. However, due to the simplification of the data sources, its learning characteristics are single, resulting in inaccurate capture of peak flow (Azar et al. 2022). On the other hand, the distributed hydrological model uses physical parameters as input, including land use and geographic elevation, which leads to a more realistic reconstruction of the basin's hydrological cycle.
Figure 8

Comparison chart of the two model simulations.

Figure 8

Comparison chart of the two model simulations.

Close modal

Analysis of the mechanisms driving the hydrological condition at multiple timescales

Analysis of the driving mechanisms of the hydrological conditions based on the WOA-RF model

The WOA-RF model was used to simulate the runoff in the Wu River basin from 1990 to 2019 and to quantitatively attribute the impact of climate and human activities on runoff at different timescales. The results are shown in Figure 9(a). The absolute change in runoff shows a phenomenon of being more in summer and less in autumn and winter, and the trends of the fundamental difference in runoff due to climate change and human activities are consistent.
Figure 9

Contribution values and impact weights for the amount of change calculated according to WOA-RF: (a) the monthly contribution value and (b) the annual, monthly, and quarterly impact weights.

Figure 9

Contribution values and impact weights for the amount of change calculated according to WOA-RF: (a) the monthly contribution value and (b) the annual, monthly, and quarterly impact weights.

Close modal

The absolute change in runoff caused by climate change peaked in July at 801.8 m3/s, while the change in runoff in February, March, and December approached zero. As shown in Figure 9(b), the relative contribution rate of runoff is proportional to the absolute contribution amount, and in months with higher total contribution, the relative contribution rate is also relatively large. Among them, the relative contribution of climate change in August and September reached more than 80%, while the relative contribution of climate change in March, April, October, and November was only about 45%. The annual contribution rate of climate change was 81.8%, and the annual contribution rate of human activities was 18.2%. This indicates that climate change is the dominant factor in the changes of runoff in the Wu River basin, and the period with the most significant impact of climate change is the flood season. As shown in Figure 9, the contributions of climate change and human activities to runoff changes at monthly, seasonal, and annual scales have different expressions before and after the abrupt change. At the monthly scale, the relative contribution of climate change is high during the flood season and low during the nonflood seasons. On the seasonal scale, the relative contribution of climate change is considerable in spring and summer but small in autumn and winter. At the annual scale, climate change is the dominant factor of runoff change in the Wu River basin.

Analysis of the driving mechanisms of the hydrological conditions based on the WEP-L model

The results of the WEP-L model presented in Figure 10(a) show that climate change plays a dominant role during the flood season, while land use shows significant differences between the flood and nonflood seasons, further demonstrating that the relative contribution rate of the WEP-L model varies significantly at different timescales (Figure 10(b)). At the annual scale, the relative contribution rate of climate change reached 70.3%. In comparison, the relative contribution rate of land use to runoff changes was only 29.7%, indicating that climate change is the main factor affecting runoff changes at the annual scale. At the seasonal scale, the effective contribution rate of climate change was higher in spring and summer and there was a lower contribution rate in autumn and winter. On a monthly scale, the relative contribution rate of climate change reached over 80% in August and September, with a peak of 82.3% in August. The results of the relative contribution rate at the monthly scale are consistent with those at the seasonal scale, indicating that runoff changes are greatly affected by seasons.
Figure 10

Contribution values and impact weights for the amount of change calculated according to WEP-L: (a) the monthly contribution value and (b) the annual, monthly, and quarterly impact weights.

Figure 10

Contribution values and impact weights for the amount of change calculated according to WEP-L: (a) the monthly contribution value and (b) the annual, monthly, and quarterly impact weights.

Close modal

Hydrological conditions and two-model attribution analysis

Climate change and human activities have jointly changed river runoff conditions, and quantifying the impact of climate change and human activities on river runoff changes is essential for water resources management and river ecological conservation (Duan et al. 2016; Yuan et al. 2018; Singh & Jain 2020). The analysis of the hydrological condition in the Wu basin before and after 2005 based on 32 IHA hydrological indicators revealed an overall change in the hydrology of 46% (moderate change). A noticeable change in the hydrological condition in the Wu River basin before and after the abrupt change provides a basis for subsequent analysis of runoff changes.

The WOA-RF model is based on a comparative analysis of the reconstructed natural runoff and the measured runoff and calculates the contribution values of climate change and human activities at the monthly scale and the impact weights at the annual, seasonal, and monthly scales. At the monthly scale, human activities and climate change increase simultaneously and peak during the flood season, with the contribution of climate change and human activities to runoff changes increasing by 893.1 and 702.8 m3/s, respectively, from May to July (Figure 9). This phenomenon is triggered by reservoir scheduling in the upper and middle reaches of the reservoir complex (Contreras et al. 2021). To prevent flooding, reservoir complexes store water at the end of the flood season to cope with the shortage of water for domestic and productive use during the nonflood season, while rainfall is also concentrated during the flood season, making the interaction between climate change and human activities maximum during the flood season (Sulis 2017), which in turn leads to a simultaneous increase and peak in the contribution of the different drivers. From May to July, the increase in the contribution of human activities to the change in runoff is higher than that of climate change, indicating that human activities during the flood season have intensified, and the degree of their changes is higher than that of climate change.

The WEP-L model is based on meteorological data and land-use data to learn about the meteorological geographic characteristics of a watershed. This study used the WEP-L model to explore the effects of climate change and land-use changes on runoff, using meteorological data in the change period and land-use data in the base period (Marhaento et al. 2017). The WEP-L model weights the impact of climate change on summer runoff at the seasonal scale at only 55.6%. On the one hand, the construction and operation of numerous reservoir clusters after the mutation year expanded human involvement in runoff changes. On the other hand, due to the high peak of natural runoff recovered based on meteorological data, the base flow is biased when calculating the impact weights of different drivers, resulting in a relatively small contribution of climate change during the flood season. At the annual scale, climate change was the main factor (70.3%) causing runoff changes. The WOA-RF model and the WEP-L model simulated runoff for the same watershed to show that WEP-L, due to the extra consideration of land use, had a lower peak runoff. The peak runoff in the simulation period is lower than that in the WOA-RF model. However, the simulation effects and correlations between the two models are generally consistent, indicating that the attribution of runoff changes based on simulation-based runoff calculations is reliable. Guo et al. (2021a) calculated the contribution of climate change and subsurface changes in the Wu River to the reduction of runoff at 73.1% and 26.9%, respectively, based on Buteyko, and the results support the conclusions of this study. The relative contribution of climate change at the annual scale calculated by the WEP-L model is about 10% higher than that of the Buteyko and WOA-RF models, which is because the Buteyko and WOA-RF models do not separate land use from human activities.

Underlying surface changes

Land-use changes affect the hydrological cycle and alter hydrogeological conditions and water availability, thus influencing runoff changes in the basin. The land-use distribution in the Wu River basin is in the following order: forest land (53.63%), cropland (28.52%), grassland (16.44%), construction land (1.03%), water (0.37%), and unused land (0.01%) (Figure 11), with the most significant change being the conversion between forest land, cropland, and grassland. Between 1990 and 2015, 9,300 km2 of grassland was converted to forest land, 3,816 km2 of cropland was converted to grassland, and 67 km2 of forest land and 82 km2 of cropland were converted to building land. The significant increase in grassland and woodland areas will enhance the potential evapotranspiration (ET0) of the vegetation in the basin. The increase in the built-up land will contribute to the formation of water production in the basin. Therefore, land-use change analysis is crucial to analyzing the factors influencing runoff change. This result further supports the conclusion that the increase in built-up land area is the primary driver of runoff due to the policy of returning farmland to forest and grass since 1990 in the Wu River basin (Xiong et al. 2020).
Figure 11

WEP-L model chord diagram of land-use change over time.

Figure 11

WEP-L model chord diagram of land-use change over time.

Close modal

Uncertainty analysis

This study also has limitations in terms of model uncertainty. Both data-driven and distributed hydrological models require parameter adjustments when simulating runoff, and the magnitude and the size of parameter adjustments are uncertain and need consistent standards. For example, the depression storage coefficient in the WEP-L model did not undergo field surveys and investigations but was only trained based on experience. This resulted in errors in the runoff calculation and simulation uncertainty. However, the tension generated in the modeling process of this study had minimal impact on the model's output, and it reflected that our quantification of the driving factors of runoff changes in the Wu River basin at a delicate timescale is reliable.

In this study, we used IHA indicators to analyze changes in river hydrological conditions. We constructed an integrated framework to quantitatively assess and analyze the causal drivers of runoff changes in the Wu River basin at different timescales based on the strengths of the WOA-RF and WEP-L models to make the results more comprehensive and reliable. This study used an integrated framework to assess the attribution of runoff changes. The results show that the Wu River flow series changed abruptly in 2005, and the hydrological situation of the river changed dramatically after the abrupt change. Seven of the hydrological indicators in the IHA metrics have high variability, 16 have medium variability, and nine have low variability. The combined level of hydrological variability in the watershed is moderately altered. It was also found that at the annual scale, the results of both models showed climate change to be the main driver of runoff alteration. The relative contribution of climate change at the yearly scale was 81.8% and 70.3% for WOA-RF and WEP-L, respectively. The attribution results of the two models at the seasonal scale showed that climate change in summer was the dominant factor affecting runoff change, and its absolute change to runoff change was −1,502.9 and −1,499.9 m3/s for WOA-RF and WEP-L, respectively. Anthropogenic activities significantly impacted runoff changes in autumn, with absolute changes of −675.9 and −839.8 m3/s for WOA-RF and WEP-L, respectively. On the monthly scale, the results of the two models showed that the impact of climate change on runoff was more significant during the flood season than during the nonflood season, and their absolute changes peaked in July at −801.8 and −802.0 m3/s, respectively.

Not required as the study did not involve human or animal.

The authors have consent to participate any offer by the journal.

The authors are giving consent to publish the article in the submitted journal.

H.W.: funding acquisition, project administration, resources, investigation, supervision. S.C.: conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, writing – original draft, and writing – review and editing. N.H.: investigation, formal analysis, methodology, validation, and visualization. L.H.: structure design and method determination. H.Y.: in-depth exploration and interpretation of the paper results. F.H.: data collection and processing. Y.M.: academic exchanges and paper discussions. W.C.: revision and review of papers. W.G.: funding acquisition and project administration.

This study was supported by the National Natural Science Fund of China (51779094); the 2016 Henan University Science and Technology Innovation Talent Support Plan (16HASTIT024); and the Guizhou Provincial Water Resources Department 2020 Water Conservancy Science and Technology Project (KT202008).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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