Characteristics of changes in the hydrological regime in the Zi River Basin and analysis of their attribution

The hydrological regime of a river is a key indicator for ensuring the ecological functioning of the river. It maintains the sustainable development and utilization of water resources and is the basis for many organisms' survival, reproduction, and development (Sun et al. 2014). With increasing climate change and anthropogenic disturbances, globally important rivers such as the Mississippi River (Haider et al. 2025), the Nile (Fathi et al. 2019), the Yellow River (Zhou et al. 2017), and the Yangtze River (Guo et al. 2021) have all experienced different degrees of changes in their hydrological regime. The impacts of climate change and human activities are the two main drivers of river flow (Wei et al. 2020). In recent decades, with socio-economic development and population growth, intense anthropogenic disturbances have altered the hydrological cycle processes in the basin (Guan et al. 2021), which has changed the ecohydrological regime of rivers to varying degrees (Sharifi et al. 2024). Therefore, analyzing changes in hydrological regimes and distinguishing between the impacts of human activities and climate change on hydrological regimes is important for the healthy development of river hydrology.

In recent years, scholars at home and abroad have discussed the quantitative analysis of river hydrological regimes. Richter et al. (1996) proposed the Indicators of Hydrologic Alteration (IHA), which are the most widely used, and the index system contains 33 indicators divided into five groups (flow, event, frequency, duration, and rate of change) and further simplified the common 170 hydrological indicators. It uses changes in river flow and external ecological change to determine a basin's overall degree of change. Later, the IHA method combined with the range of variability approach (RVA) method for univariate and integrated hydrologic evaluation was proposed by Richter et al. (1998). Gunawardana et al. (2021) quantified the pre- and post-impacts of the transboundary Srepok River Basin hydropower development on the hydrologic cycle using the RVA in IHA. Although the IHA system is relatively well developed, there are problems of autocorrelation and overlapping information between IHA variables (Smakhtin et al. 2006). The most ecologically relevant hydrologic indicators (ERHIs) have received more and more attention from researchers. Jiang et al. (2022) screened seven indicators as ERHIs for the Weihe River using principal component analysis (PCA). Li et al. (2022) studied the Han River Basin by selecting strong variability indicators from ERHIs and diagnosing watershed variability with a strong variability index. Replacing the large number of IHA with ERHIs facilitates the study by retaining as much key information as possible from the original data and significantly reducing the redundancy of information from the large number of hydrological variables (Yang et al. 2024).

Meanwhile, for the attribution analysis of river flow changes, scholars in recent years have mainly used various hydrological models and elasticity coefficient methods to analyze the influencing factors of flow changes in different rivers. Mirjalili & Lewis (2016) proposed a new population intelligence optimization algorithm, the Whale Optimization Algorithm (WOA). Wang et al. (2023a, 2023b, 2023b) proposed a method for predicting monthly flow intervals based on the long short-term memory (LSTM) model optimized by WOA and nonparametric kernel density estimation. The Random Forest (RF) model proposed by Wang et al. (2022) for attributing flow changes in the upper Yongding River Basin provides a new method and idea for quantitatively identifying the contribution of flow changes in the basin. Soo et al. (2024) found that the RF model exhibited the best performance in flow simulation and prediction in the Klang River Basin.

Several scholars have studied the hydrological regime of the Zi River Basin from diverse perspectives. Wang et al. (2019) evaluated the hydrological regime using the IHA-RVA method. Meanwhile, Li et al. (2018) employed the cumulative distance level method and the sliding t-test to analyze trends and detect abrupt changes in precipitation and flow series over the past 55 years within the basin. Notably, most hydrological researchers have confined their analysis of flow changes to identifying trends and mutation points (Li et al. 2018; Wang et al. 2019). There is a pressing need for a more in-depth exploration of the variability of various hydrological indicators of flow and the extent of these variations. Furthermore, even when this issue is addressed, the overlap among hydrological indicators must be examined, a particularly acute problem in the Zi River Basin. Additionally, a quantitative analysis of the impacts of human activities and climate change on hydrological changes in the Zi River Basin remains an area requiring further research.

In this study, a strategy based on PCA to select ERHIs combined with the WOA-RF model is proposed to address the above problems, which effectively solves the problem of variable redundancy of traditional IHA method through PCA dimensionality reduction and improves the scientificity of hydrological variability assessment; meanwhile, combining with the WOA-optimized RF model, the generalization ability and reliability of flow attribution analysis are significantly enhanced. Compared with the traditional IHA or single RF method, the optimization and breakthrough of the methodology are achieved.

Therefore, given the limitations of previous studies, the study's main objectives are the following: (1) to analyze the characteristics of long-term series changes of hydrometeorological elements in the Zi River Basin. (2) Access the changes in the hydrological regime of Zi River before and after the sudden change based on IHA and select the indicators of ERHIs of the Zi River Basin by PCA to reduce the influence of autocorrelation information on the assessment results. (3) To reconstruct the natural flow in the Zi River Basin based on the WOA-RF model and to evaluate the impacts of climate change and human activities on the changes in the hydrological regime in the Zi River Basin. This study helps us systematically understand the characteristics of changes in the hydrological regime of the Zi River Basin. The results can provide a new approach to studying hydrological changes in the Zi River Basin and are of great reference value for watershed ecological protection and water resource management. Additionally, they serve as a guide for similar research endeavors.

The data used in this study include meteorological data and hydrological data. We collected daily flow data from the Taojiang Hydrological Station in the Zi River Basin, spanning from 1961 to 2017, and this data will be used to analyze the changing pattern of flow in the Zi River Basin. The Taojiang hydrological station is located at the downstream outlet of the Zi River Basin, and its long-term flow data were obtained from the Hunan Provincial Bureau of Hydrology and Water Resources Survey. In addition, the day-by-day meteorological data from 1961 to 2017 were obtained from five meteorological stations: Anhua, Xinhua, Wugang, Shaoyang, and Dongkou. The meteorological data were obtained from the raw data of day-by-day observations of 824 national basic meteorological stations provided by the China Meteorological Network (http://data.cma.cn/), covering indicators such as precipitation, average temperature, relative humidity, wind speed, sunshine duration, and potential evapotranspiration. The geographical locations and altitudes of all the above meteorological stations are shown in Table 1.

The Mann–Kendall (M–K) test is a typical nonparametric test that can be used to test for trends and significance in hydrological and meteorological time series (Pirnia et al. 2019). The test steps are as follows:

• (1) Set up a time series {X_t} and a sequence of observations {x_t, t= 1, 2, …, n} to construct a sequential column:

  

  (1)
• (2) Under the assumption that the time series are randomly independent, the statistics are defined as follows:

  

  (2)

The cumulative anomaly method is mainly applied to hydrometeorological studies. It validates the ranking of cumulative anomalies by evaluating their dispersion X_t concerning the mean value of the data. In the case of X_t decreasing, it indicates values below the data mean, while its increasing indicates values above the mean. Fluctuation plots of their long-term series can show increases and decreases in the data. In general, the curve fluctuations reflect the timing of hydrological variations, and the mutation points usually correspond to the turning points of the curve or positions where the slope changes significantly (Wang et al. 2023a, 2023b, 2023b).

Equation (7) follows a t-distribution with ⁠. Given a threshold value ⁠, when ⁠, it is considered to be a hydrological mutation point.

In this equation, ‘W’ is the wavelet transform coefficients; ‘f(t)’ is the signal or square productable function; ‘a’ is the scale factor, reflecting the period length of the wavelet; ‘b’ is the translation factor, reflecting the time translation. ‘’ is the complex conjugate of ‘’.

Richter et al. (1996) proposed that the IHA reflect rivers' hydrological regimes. They include indicators of flow, events, frequency, and rate of change, as shown in Table 2.

P_o denotes the frequency with which a hydrological parameter falls within the flow management target range following a hydrological change. P_e denotes the expected value, which is the range of the ecohydrological target interval. The expected value for this study is 50%. D_i is a positive value, which indicates that the parameter of the hydrological sequence after variation falls within the target interval higher than the expected value; D_i is a negative value, which indicates that the parameter of the hydrological sequence after variation falls within the target interval lower than the expected value. When ⁠, the hydrological parameters are considered to be highly variable (H); when ⁠, the hydrological parameters are considered to be moderately variable (M); when ⁠, the hydrological parameters are considered to be lowly variable (L).

Black et al. (2005) proposed another broad hydrological alteration index, the Dundee Hydrological Regime Assessment Method (DHRAM), based on the five groups of 32 parameters of IHA, to measure the degree and extent of anthropogenic alteration of hydrological regimes. DHRAM classified the mean and coefficient of variation of the five groups of 32 parameters of IHA into three impact categories: Level 1 (one impact point with a low threshold), Level 2 (two impact points with a medium threshold), and Level 3 (three impact points with a high threshold) (Table 3). Summing the mean and coefficient of variation impact categories for each group of indicators resulted in a score (score range 0–30), by which the degree of impact was assessed for each of the five classes. The higher the score, the more significant the change in the hydrological situation of the river and the greater the likelihood of damage to the river ecosystem.

PCA transforms a large number of correlated variables into a few uncorrelated variables through the idea of dimensionality reduction. It retains as much information as possible in the original data and the principal components (PCs). PCs are the new few composite variables, each a linear combination of the original variables with important applications in fields such as hydrology and ecology (Kholoosi et al. 2016; Yang et al. 2017). After dimensionality reduction, the indicators obtained by the IHA system are called the most ERHIs. These indicators represent many hydrological variables through fewer key hydrological variables and can reflect the most important ecologically relevant information. The ecologically relevant information reflected by the ERHIs screened in this paper is shown in Table 4. The specific process of the PCA implementation and the principle and methodology can be found in the literature (Shlens 2014).

The WOA consists of three phases: encircling predation, bubble net predation, and search predation.

The RF model and the Whale algorithm optimization allowed for a more accurate reconstruction of natural flow. The WOA-RF model in this study includes precipitation, mean air temperature, relative humidity, wind speed, sunshine, and potential evapotranspiration, and the output factor is monthly flow. Based on artificial parameter optimization, the monthly mean natural flow for 1961–2017, considering only the effect of climate change, was recovered using the monthly mean flow before the mutation and the precipitation, mean air temperature, relative humidity, wind speed, insolation, and potential evapotranspiration after the mutation.

In the above equation, and denote the observed and simulated flow values (m³/s), respectively, and and denote the average observed and simulated flow values (m³/s), respectively.

The degree of hydrological change was derived using the DHRAM method (Table 7). From the study results, the total DHRAM score of Taogang station is 4, and the degree of change is 3, which is low. Considering the 32 hydrological change indicators in the IHA, the overall change in the degree group is more moderate than low change, and combining the results of the two methods, it is determined that the degree of change in the hydrological situation of the reservoir is moderate.

This study used PCA to screen eight ERHIs in the Zi River Basin. The eight metrics are median flow in March, median flow in May, median flow in September, minimum 30-day flow, maximum 3-day flow, number of low-flow pulse occurrences, number of high-flow pulse occurrences, and average rate of increase. Median monthly flows (median flows in March, May, and September) mainly affect habitat availability for riverine aquatic organisms as well as water affecting water temperature, oxygen levels, photosynthesis in the water column (CONSERVANCY T N 2009); minimum 30-day flow, maximum 3-day flow are related to channel morphology and structure and natural habitat conditions (CONSERVANCY T N 2009); the number of low-flow pulse occurrences, and the number of high-flow pulse occurrences mainly affects aquatic organisms habitat availability in the river rookery as well as the relationship between the river and the nutrient and organic matter exchanges between the river and the rambler (CONSERVANCY T N 2009); The average rate of increase, in turn, correlates with the trapping (rising numbers) of organisms on islands and flood plains (CONSERVANCY T N 2009). Gao et al. (2009) found some variation in the results of analyses across watersheds and datasets. However, it tended to include three common elements: at least one monthly mean flow statistic, at least two extreme event statistics representing high and low extremes, and at least one statistic correlating the frequency of low and high flows. It agrees with the eight ERHIs initially selected for this paper.

Previous studies have established that human activities are the primary factor influencing changes in the hydrological regime of the basin. These activities directly or indirectly affect the quality, quantity, and flow processes through the construction of hydraulic infrastructure and modifications to subsurface conditions (Fan et al. 2023). Li et al. (2018) excluding evapotranspiration from their analyses, quantitatively assessed the impacts of precipitation and human activities on flow changes during the variability period in the Zi River Basin. They concluded that human activities were the predominant contributors to these changes. Further research has underscored that the alterations in flow within the Zi River Basin are primarily attributable to human interventions, including reservoir construction, urbanization, artificial water consumption, and other integrated factors (Li 2018). While the present study's findings align with these conclusions, notable differences may stem from the exclusion of evapotranspiration in prior studies, variations in the definition of base and variant periods, and differences in the data series and methodologies employed. Nonetheless, the overarching consensus remains that human activities are the dominant factor driving flow variability. The downstream of the Zi River Basin in Yiyang City has constructed terrace power stations, such as Zhexi and Majitang (Li et al. 2018). Under the effect of the reservoirs and dams, the average flow in May of the flood season was reduced, and the average flow in March of the dry season was slightly increased. The analyses in the ERHIs section of this study are in agreement. After the construction of a reservoir on a river, there is a tendency for the flow volume to decrease significantly, especially when it is first put into operation. However, this tendency will gradually slow down or rebound after regular operation. Therefore, the direct effect of dam construction on total flow is limited (Chen et al. 2021). However, in the 1990s, the Zhexi Reservoir needed more flood control capacity and high discharge. The Zi River Basin often suffered from floods, which, to some extent, increased Zi River flow (Xiong & Liu 1999).

In this study, we propose a structured and robust framework for assessing the hydrological regime change and its ecological impacts. It can be adapted to a wide range of watersheds with different hydrological, climatic, and anthropogenic disturbances. PCA can be applied across watersheds to screen for ERHIs. Still, initial hydrological indicators need to be selected in accordance with the characteristics of the watersheds, e.g., in arid zones, priority should be given to the extreme flow indicators. The WOA-RF model can effectively differentiate the effects of human activities and climate change on flow and is applicable to basins with different climatic conditions. However, the implementation of the WOA-RF model needs to pay attention to the availability of data, the main drivers, and the model's calibration. Alternative methods or small-scale datasets can be explored for data-scarce basins, and basins dominated by climate change should incorporate more climatic variables. The model should be recalibrated so that each basin can adapt to its hydrological processes.

In order to quantitatively analyze the characteristics of hydrological regime changes in the Zi River Basin and the degree of influence of climate change and human activities on the hydrological regime changes in the Zi River Basin, we started with the inter-annual characteristics of hydrological regime changes. On this basis, the key ERHIs were selected to reduce the influence of redundant information on the hydrological regime change. The WOA-RF model was also used to quantitatively separate the degree of changes in the flow of the Zi River due to human activities and climate change. The main conclusions are as follows:

• (1) The mutation results show that the flow in the Zi River Basin underwent a mutation in 1987. The overall mean annual flow and mean annual precipitation in the basin showed a decreasing trend, and the potential evaporation showed an increasing trend. The results of wavelet analyses indicated significant cycle changes on time scales of 29 and 5 years, with average cycles of 25 and 4 years.

• (2) Combining the IHA-RVA and DHRAM methods, the 32 ecohydrological indicators in the Zi River Basin were mainly medium and low changes, with an overall change degree of 42%, and the total DHRAM score was 4. After the hydrological indicators were preferred, eight ERHIs were identified, with a combined change degree of 48.5%, a medium change. Compared with the traditional RVA method and DHRAM method, the degree of change of the hydrological regime of Zi River is more intuitively shown.

• (3) The results of the WOA-RF model showed that climate change caused a downward trend in flow, while human activities caused an upward trend in flow. Meanwhile, the results of attribution analysis showed that the contribution of human activities and climate change to the change of flow of the capital water was 71.2 and 28.8%, respectively, and that human activities were the main factor affecting the change of flow of the capital water. The construction of reservoirs was the most significant factor contributing to the impact of human activities, with a 65.6% contribution.

The authors thank their brothers at North China University of Water Resources and Electric Power for their comments and help with this study.

This study was supported by Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (24ZX007).

X.Z. conceived the study and wrote the first draft, X.Z. and X.B. collected the data and performed the analysis, L.H. and X.J. analyzed the methodology, W.G. and H.W. supervised the paper, and all authors provided comments and assistance on the first few versions of the manuscript. All authors read and approved the final manuscript.

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

The authors declare there is no conflict.