## Abstract

Ecological flow is a key element in maintaining the biodiversity of a basin's aquatic ecosystem. This study quantifies the degree of hydrological regime alteration in the Jialing River and the contributions of various driving factors using the indicators of hydrologic alteration and range of variability approach (IHA-RVA), the river impact method (RI), and the elasticity coefficient method based on long-term hydro-meteorological data. Cross-wavelet analysis is used to reveal the periodicity and coherence of runoff and driving factors. Additionally, the improved *Q*_{90} method presents a more effective annual ecological flow process. The results show that the overall hydrological regime in the Jialing River has undergone a moderate change, with RVA and RI values of 45.02% and 0.66. Human activities contribute 54.68% to the changes in streamflow. Streamflow has an extremely significant correlation with precipitation and aridity index (Φ). Approximately 85% of the wavelet cone of influence (COI) area, tested at a 95% confidence level, falls within the region. The study also found that the ecological flow obtained through the improved *Q*_{90} method is more conducive to the healthy development of the river ecosystem in the Jialing River. These findings can provide some assistance for aquatic ecosystem restoration in the Jialing River.

## HIGHLIGHTS

To research and assess the changes in the Jialing River's hydrological regime, 62 years' worth of daily runoff data from the river's mainstream were used in this study.

This study quantitatively evaluated the effects of both climate change and human activities on runoff and concluded that the effects of human activities have slowed.

In this study, the Cross-Wavelet analysis is used to thoroughly analyze the resonance cycle and correlation between runoff depth and each driving element. As a result, the mutual influence law between runoff depth and each driving factor of the Jialing River mainstream is revealed.

An intra-annual ecological flow process suitable for the Jialing River is proposed, and the ecological flow guarantee degree under multiple scales is analyzed.

## INTRODUCTION

Adequate flows meet the habitat and food needs of riverine fish and other aquatic organisms. They are critical to maintaining the health of river ecosystems, but many rivers face the problem of reduced flow (Doll *et al.* 2009). The natural flow of a river refers to the flow that has evolved over a long period to meet the needs of its ecosystem. However, in recent years, due to large-scale human development and utilization of water resources, as well as the impact of climate change, the natural hydrological regime in the majority of rivers has been altered. This has led to a scenario where the flow is insufficient to maintain the minimum ecological requirements of the river, i.e., the river's ecological flow requirements are not being met.

The quantification of the degree of hydrological regime alteration and the identification of driving factors are important prerequisites for the development of river ecological flow. Richter *et al.* (1996) proposed the indicators of hydrologic alteration and range of variability approach (IHA-RVA) to quantify the degree of hydrological regime alteration, which has been widely applied. Based on this, Guo *et al.* (2022b) used IHA-RVA to evaluate the degree of hydrological regime alteration in the upstream Min River of the Jialing River, which is 45% and considered moderate. In addition, the RI method is another effective approach for evaluating hydrological regime alteration. Since 2000, with the increasing impact of human activities on river ecological systems, the RI method has gradually developed in the direction of an ecologically comprehensive evaluation, taking into account societal, economic, and environmental factors. As a result, it has become one of the most important river ecological evaluation methods in the world (Haghighi & Klove 2013).

For the attribution analysis of runoff changes, some scholars have reported that climate change is a significant factor influencing hydrological regime alteration upstream of rivers, while human activities are significant factors affecting hydrological regime alteration downstream (Melo *et al.* 2023). The elasticity coefficient method and hydrological models are commonly used methods in runoff change attribution analysis. Compared with hydrological models, the parameters of the elasticity coefficient method are easier to obtain. The improved elasticity coefficient method has been extended to various climatic factors and has been widely used in quantifying the impacts of human activities and climate change on runoff. Among them, the elasticity coefficient method based on the Budyko theory has been widely used due to its good applicability. Dai *et al.* (2023) analyzed the contribution rates and spatial differences of climate and human activities to the runoff changes in the main tributaries of the middle reaches of the Yellow River based on the Budyko theory. Although many studies have investigated the attribution identification of runoff changes from different perspectives, fewer studies have evaluated the periodicity of each factor such as precipitation on runoff changes and coherence analysis, and served for sustainable management of watersheds. Cross-wavelet analysis was developed in the 1990s, which combines the ideas of wavelet analysis and the concept of coherence analysis to analyze the time-frequency characteristics of two signals and the relationship between them simultaneously. Currently, the cross-wavelet analysis is widely used in the field of signal processing and data analysis. It can be used in fields such as the analysis of climate data to reveal correlations and common features between signals.

To maintain the health and biodiversity of river ecosystems and to mitigate the effects of reduced flows on rivers, many studies have been conducted on ecological flows from different perspectives (Lu *et al.* 2021; Zhang *et al.* 2023a). Among them, Jiao *et al.* (2023) integrated the characteristics of river organisms and their habitat requirements to refine the proposed ecological flow requirements of rivers at different times, which helps river ecosystems maintain homeostasis. Therefore, to cope with the impact of runoff reduction on river ecosystems, this study regulates runoff by integrating various techniques to achieve the protection of river ecosystems.

Compared with previous studies on the Jialing River, this study combined the IHA-RVA method with the RI method to establish an integrated assessment framework to comprehensively evaluate the degree of change in the hydrological regime of the Jialing River. When using the Budyko theory to investigate the influence of human activities and climate change on the driving force of runoff changes, this study evaluates in detail the influence of changes in subsurface conditions on runoff changes and analyzes the resonance period and correlation between runoff and underlying surface parameter and aridity index by combining the cross-wavelet analysis, which fills the gap of the research in this area. Abdourahamane & Acar (2018) used cross-wavelet analysis to analyze meteorological drought trends, periodicity, and relationships with 10 ocean-atmosphere variables based on a 3-month scale standardized precipitation index calculated from rainfall data at 13 stations in Niger. Szolgayova *et al.* (2014) found a strong correlation between precipitation and flow spectra in the low-frequency interval based on cross-wavelet and wavelet coherence spectra.

The Jialing River is the largest tributary of the Yangtze River basin and the main source of runoff for the upper reaches of the Yangtze River. Therefore, studying the hydrological regime and ecological flow requirements in the Jialing River basin is of great practical significance. The purpose of this study is to quantify and separate the degree of hydrological alteration of the Jialing River and the effects of climate change and human activities on its runoff over a long time series, and to reveal the periodicity and correlation of runoff with precipitation, potential evapotranspiration, underlying surface parameter (*n*) and aridity index (Φ) by the cross-wavelet analysis, and to propose the regulation of runoff through the intra-annual ecological flow process. This study has important practical significance for protecting the health of river basins, realizing sustainable development in the basin, and restoring habitats.

## STUDY AREA AND DATA

### Study area

^{2}, which is the largest river basin in the tributaries of the Yangtze River (Figure 1). Most of the Jialing River basin has a subtropical humid monsoon climate, the basin for many years an average temperature of 17.6 °C, and the average wind speed of the Jialing River basin is 0.9–2.1 m/s throughout the year, which is one of the areas with the smallest wind speed and the highest frequency of static wind in the country. The annual precipitation in the basin is more than 1,000 mm, which is concentrated from May to October. The runoff of the Jialing River mainly comes from rainfall, which is relatively abundant, and the flood is characterized by a short duration and a high peak flood. Beibei Hydrological Station is the downstream control station of the mainstream of the Jialing River, about 53 km away from the confluence of the Jialing River and the Yangtze River (Chaotianmen, Chongqing), and it is the main outlet control station of the Jialing River basin, controlling 98% of the runoff in the basin.

### 2.2. Data

The daily streamflow data used in this study were selected from the daily data of the Beibei hydrological station from 1956 to 2018, sourced from the ‘Hydrological Yearbook of the Yangtze River Basin’. The precipitation, evapotranspiration, and other data used in this study were sourced from daily data collected from 11 meteorological stations in the Jialing River basin, including Wudu and Mianyang, from 1962 to 2017, obtained from the China Meteorological Data Service Center (http://data.cma.cn/), and the digital elevation model (DEM) data from the Geospatial Data Cloud (https://www.gscloud.cn/).

## STUDY METHODS

### Hydrological regime quantification method

*et al.*2002). However, the M-K test may have more than one change point during the test, so further verification is needed. Therefore, in this study, the sliding

*T*-test was introduced to further verify the results of the M-K test. The sliding

*T*-test is a method of constructing a test statistic

*T*and observing whether the

*T*statistic exceeds the significant level to determine whether a certain year is an abrupt change year (Lu

*et al.*2021; Zhang

*et al.*2023a). After determining the abrupt change year, this study used the IHA-RVA method to quantitatively analyze the hydrological regime of the river from aspects such as flow rate, frequency, time, high-low pulse flow rate, and its occurrence time, and daily variations of river hydrological flow rate amplitude, and other indicators as shown in Table 1. The formula for calculating the degree of hydrological change is as follows; Guo

*et al.*(2022a, 2022b) used a similar equation in their study:where represents the degree of change of the indicators

*i*; represents the overall change in the hydrological regime; represents the number of years that the indicators

*i*of the evaluation period is within the RVA threshold; represents the expected number of years falling within the RVA target range after the abrupt change year;

*r*represents the proportion of the runoff indicators

*i*falling within the RVA threshold range before the change, and the expected number of years in this study is 50% of the total number of years in the evaluation period. represents the total number of years after the change;

*i*= 1, 2, … , 32.

IHA parameter group . | Parameter indicators . |
---|---|

Group 1: Monthly average flow | Average monthly flow |

Group 2: Annual extreme flow | Minimum and maximum flow and base flow index at 1, 3, 7, 30, and 90 days per year |

Group 3: Time of annual extreme flow | Date of annual maximum and minimum 1-day flow |

Group 4: Frequency and duration of high and low pulses | The average number of high and low pulses per year and pulse duration |

Group 5: Flow change rate and frequency | Annual medians of rise, decline, and reversals |

IHA parameter group . | Parameter indicators . |
---|---|

Group 1: Monthly average flow | Average monthly flow |

Group 2: Annual extreme flow | Minimum and maximum flow and base flow index at 1, 3, 7, 30, and 90 days per year |

Group 3: Time of annual extreme flow | Date of annual maximum and minimum 1-day flow |

Group 4: Frequency and duration of high and low pulses | The average number of high and low pulses per year and pulse duration |

Group 5: Flow change rate and frequency | Annual medians of rise, decline, and reversals |

When evaluating the results, changes between 0 and 33% are classified as low-degree change (L); changes between 33 and 67% are classified as moderate-degree change (M); changes between 67 and 100% are classified as high-degree change (H).

On the other hand, considering that there are many hydraulic structures built in the Jialing River, the impact of hydraulic engineering on the natural hydrology of the river mainly manifests in the size of the flow rate, the occurrence time of extreme values, and the annual variation of flow rate. For these changes, corresponding impact indicators can be used to quantify them, among which the magnitude impact factor (MIF), timing impact factor (TIF), and variation impact factor (VIF) can be used to represent the impact of flow rate in terms of magnitude, time, and variation within the year, respectively. The river impact (RI) method comprehensively considers these three main influences, so this study further evaluates the impact of hydraulic engineering on the hydrological regime using the RI method (Haghighi & KloVe 2013; Haghighi *et al.* 2014).

When evaluating the results, an RI value between 0.75 and 1 is classified as a low-degree change, an RI value between 0.5 and 0.75 is classified as a moderate-degree change, an RI value between 0.25 and 0.5 is classified as a high-degree change, and an RI value between 0 and 0.25 is classified as a severe change.

### Budyko theory

*et al.*2008), known as the Choudhury–Yang formula (Choudhury 1999; Roderick & Farauhar 2011):where

*E*represents the long-term average annual evapotranspiration,

*P*represents the long-term average annual precipitation,

*E*

_{0}represents the long-term average annual potential evapotranspiration (calculated using the Penman–Monteith formula) (Allen 2006),

*n*is a parameter that reflects the characteristics of the underlying surface of the basin, including topography, soil, and vegetation, etc. (Zhang

*et al.*2001; Yang

*et al.*2007, 2009).

In formula (3), we generally consider *P*, *E*_{0}, and *n* as independent variables. Combined with the long-term average water balance equation of the basin, *P* = *E* + *R*, the rainfall elasticity coefficient (*ε*_{P}), potential evapotranspiration elasticity coefficient (*ε _{Ε}*

_{0}), and underlying surface elasticity coefficient (

*ε*

_{n}) are defined (Li

*et al.*2021).

By taking the differential form of the water-energy balance equation and combining it with the definition of the elasticity coefficients, we can calculate *ε*_{P}, *ε _{Ε}*

_{0}, and

*ε*

_{n}.

*E*

_{0}/

*P*, then:Based on the abrupt change year, the study period can be divided into two periods, namely the base period and the human activities period, to obtain the changes in each hydrological indicator:where

*x*is

*P*,

*R*,

*E*

_{0},

*n*, respectively,

*x*

_{1},

*x*

_{2}can represent the runoff depth, basin precipitation, basin potential evapotranspiration, and basin underlying surface parameters in the base period and the human activities period, respectively; and represents the amount of change in

*R*,

*P*,

*E*

_{0},

*n*before and after the abrupt change year.

*ε*,

_{p}*ε*

_{E}_{0}, and

*ε*for the three runoff factors, changes in precipitation, potential evapotranspiration, and the underlying surface can be estimated, respectively. The specific formulas are as follows:where

_{n}*y*is

*P*,

*E*

_{0}, and

*n*, respectively, Δ

*R*represents the change in runoff caused by basin precipitation, basin potential evapotranspiration, and basin underlying surface parameters, respectively, in units of mm;

_{y}*ε*

_{y}represents the coefficient of elasticity concerning

*y*; represents the amount of change in

*P*,

*E*

_{0}, and

*n*before and after the abrupt change year, in units of mm.

*et al.*(2022b) used a similar formula in their study:where

*C*represents the rate of contribution of the change in

_{y}*y*to the change in runoff, in units of %.

### Cross-wavelet analysis

*et al.*2021).where is the complex conjugate of ; and denotes the confidence level associated with the probability

*p*of the probability distribution function, which is defined by the square root of the product of the two distributions.

### The method for calculating basin ecological flow

To cope with the negative impacts of runoff changes on the hydrological situation of the river, this study proposes that the annual ecological flow process of the Jialing River can be derived from the improved annual distribution method and the improved *Q*_{90} method to mitigate the ecological risks of climate drought and to meet the needs of fish and other living organisms, to maintain the ecosystem balance.

Traditional hydrological methods use a specific percentile of multi-year average runoff or specific guarantee rate on the natural runoff frequency curve as an indicator to calculate ecological flow, which has strong subjectivity and empiricism. Furthermore, a single annual distribution approach based only on the ratio of the mean values for the same period is not suitable for rivers with large seasonal fluctuations in runoff or with extreme values of annual mean runoff. Therefore, Dunbar *et al.* (1997) proposed to use the multi-year average monthly runoff under a 90% guarantee rate to replace the minimum average runoff in the original method, which weakened the impact of extreme runoff on calculating ecological flow and was tested according to the evaluation criteria of the Tennant method (Tennant 1976).

When using the improved annual distribution method, it is necessary to divide the annual dry, normal, and wet seasons first. According to relevant studies, the wet, normal, and dry seasons can be divided based on the standard of the departure percentiles *A*, with the division criteria being *A* > 10% for the wet seasons, −10% < *A* ≤ 10% for the normal seasons, and *A* ≤ −10% for the dry seasons.

*q*

_{90%, i,j}represents the runoff volume in month

*j*of period

*i*under 90% guarantee, with the unit of m

^{3}/s; represents the average monthly runoff volume of each period

*i*under 90% guarantee, with the unit of m

^{3}/s;

*t*represents the corresponding number of months within period

_{i}*i*, and ; represents the multi-year average runoff volume in month

*j*of period

*i*, with the unit of m

^{3}/s; represents the average monthly runoff volume of each period

*i*, with the unit of m

^{3}/s; represents the average ratio of the same periods of period

*i*; represents the ecological flow rate of month

*j*, with the unit of m

^{3}/s;

*i*is 1, 2, 3 representing the period of the wet, normal, and dry seasons, respectively, and

*j*is an integer between 1 and 12.

When using the improved annual distribution method to calculate the simultaneous average ratio, it is believed that the average runoff of the wet, normal, and dry seasons under the 90% guarantee rate is the average of the 90% guarantee rate of each month in the corresponding seasons. This results in the average runoff of the three seasons also being affected by extreme values. In this study, based on previous experience, it is proposed that the monthly average runoff of each period should be averaged separately according to the three hydrological seasons, and then the 90% guarantee rate values of the ecological flow for the wet, normal, and dry seasons should be calculated using the averaged monthly runoff values for each season as a time series. The simultaneous average ratio can then be obtained accordingly, and this approach is called the improved *Q*_{90} method.

### Tennant method

The Tennant method has low requirements for hydrological data and has therefore been widely used (Tennant 1976). In this study, the range of ecological flows obtained by the two methods was checked using the Tennant method (Table 2) (Karim *et al.* 1995; Reiser *et al.* 2011).

Flow description. . | Recommended flow rate (October–March of next year) . | Recommended flow rate (April–September) . |
---|---|---|

Monthly average flow rate percentage (%) . | Monthly average flow rate percentage (%) . | |

Maximum | 200 | 200 |

Optimal | 60–100 | 60–100 |

Excellent | 40 | 60 |

Very Good | 30 | 50 |

Good | 20 | 40 |

Sufficient | 10 | 30 |

Insufficient | 10 | 10 |

Minimum | 0–10 | 0–10 |

Flow description. . | Recommended flow rate (October–March of next year) . | Recommended flow rate (April–September) . |
---|---|---|

Monthly average flow rate percentage (%) . | Monthly average flow rate percentage (%) . | |

Maximum | 200 | 200 |

Optimal | 60–100 | 60–100 |

Excellent | 40 | 60 |

Very Good | 30 | 50 |

Good | 20 | 40 |

Sufficient | 10 | 30 |

Insufficient | 10 | 10 |

Minimum | 0–10 | 0–10 |

### Basin ecological flow guarantee degree

Currently, the definition of ecological flow guarantee degree in a study is generally the percentage of days when the actual flow of the river meets the ecological flow demand, out of the total number of days during the study period. A higher value indicates a higher ecological flow guarantee degree, which means that the flow during that period can better ensure the minimum ecological flow demand of the river, corresponding to a healthier river ecosystem (Fan *et al.*; Roozbahani *et al.* 2013).

In addition, when evaluating the ecological flow guarantee degree, the scale of previous studies was limited to the annual scale, but this study further expanded the scale of ecological flow security to the wet, normal, and dry seasons and the monthly scale, and the degree of ecological flow guarantee degree should be paid attention to ensure the sustainable utilization of water resources in the basin and the maintenance of the river ecosystem health.

*i*; represents the ecological flow guarantee degree of period

*m*of the year

*i*(

*m*is 1, 2, and 3 representing the wet, normal, and dry seasons of the year

*i*, respectively); represents the ecological flow guarantee degree of month

*j*of the year

*i*; represents the guaranteed number of days of ecological flow in the year

*i*; represents the total number of days of the year

*i*; represents the guaranteed number of days of ecological flow in period

*m*of the year

*i*; represents the total number of days of period m of the year

*i*; represents the guaranteed days of ecological flow in month

*j*of the year

*i*; represents the total number of days in month

*j*of year

*i*; represents the daily flow of the Jialing River on day

*k*of month

*j*of period m of year

*i*; and represents the ecological flow in month

*j*of year

*i*. The units of the flow rate are all m

^{3}/s.

## RESULT

### Trend analysis and abrupt change testing

*Z*= −1.7378 is obtained, indicating that the average annual runoff in the Jialing River basin is decreasing. This result has passed a significance test of 90%, reflecting a significant downward trend in the average annual runoff. The curves of UF and UB have multiple intersection points, and except for 2010, multiple abrupt change years have passed a significance test of 95%, such as the years 1976, 1980, 1986, 1989, and 2015. Through sliding T verification, the test statistic for 1986 passed a significance level of 95%, so this study identifies 1986 as the hydrological regime's abrupt change year for the Jialing River.

### Hydrological regime alteration degree analysis

There are currently more than 160 confirmed fish species in the Jialing River basin, many of which are rare or protected species. However, changes in the hydrological regime can also affect the Jialing River fish. After the hydrological regime alteration, the high flow of the river decreases and the rate of increase is reduced, which will inhibit the growth and reproduction of fish species that require rising water to stimulate spawning. In addition, the construction of water conservancy projects can also cut off the connectivity between the mainstream and tributaries, which affects the migration of fish. All of these factors have contributed to a significant decline in both the variety and quantity of fish species in the Jialing River.

According to the RI method, the degree of change in the hydrological regime in the Jialing River basin before and after the abrupt change year was calculated. The VIF, TIF, and MIF are 0.30, 0.47, and 0.86, respectively, which gives an RI of 0.66. The RI value falls between 0.50 and 0.75, indicating a moderate degree of change in the hydrological regime before and after the abrupt change year. Combining the results obtained from the IHA-RVA and RI methods, it can be concluded that the hydrological regime in the Jialing River basin underwent a moderate degree of change in 1986.

### Attribution analysis of runoff variation

*n-*value showed an overall increasing trend (Figure 4), indicating that the vegetation coverage in the Jialing River basin was generally increasing, as a larger

*n*-value indicates a greater vegetation coverage. However, the growth rate of vegetation coverage showed a slowdown after the abrupt change year, which is consistent with the findings by Xu

*et al.*(2021) on the Weihe River.

To determine the contribution of meteorological factors and human activities to runoff changes, the multi-year average potential evapotranspiration, runoff depth, precipitation, aridity index (Φ), and runoff coefficient (*R*/*P*) for the base period and human activities period can be calculated based on annual data, as shown in Table 3. Comparing the Φ and *R*/*P* of the base period with those of the human activities period, it can be seen that the *Φ* increased from 0.94 in the base period to 0.98 in the human activities period, with a change rate of 4%; the *R*/*P* decreased from 0.45 in the base period to 0.40 in the human activities period, with a change rate of −11%; and the n increased from 1.28 in the base period to 1.45 in the human activities period, with a change rate of 12%. It can be seen that both the climate conditions and underlying surface conditions have undergone significant changes before and after the abrupt change year. Based on the data in Table 3, the contribution rates of precipitation, potential evapotranspiration, and underlying surface changes to runoff changes can be calculated as *C*_{P} (46.07%), *C*_{E0} (−3.00%), and *C*_{H} (54.68%), respectively. It can be seen that the dominant factor affecting runoff changes is human activities, followed by precipitation, while the impact of potential evapotranspiration is minimal. The error between the results obtained from the Budyko theory and the actual values is only 2.25%, indicating that the results obtained in this study are reasonable.

Year . | E_{0} (mm)
. | R (mm)
. | P (mm)
. | ΔE_{0} (mm)
. | ΔR (mm)
. | ΔP (mm)
. | Φ
. | R/P
. | n
. |
---|---|---|---|---|---|---|---|---|---|

1962–1985 | 934.18 | 444.76 | 991.85 | −6.29 | −68.37 | −42.99 | 0.94 | 0.45 | 1.28 |

1986–2017 | 927.89 | 376.39 | 948.86 | 0.98 | 0.40 | 1.45 |

Year . | E_{0} (mm)
. | R (mm)
. | P (mm)
. | ΔE_{0} (mm)
. | ΔR (mm)
. | ΔP (mm)
. | Φ
. | R/P
. | n
. |
---|---|---|---|---|---|---|---|---|---|

1962–1985 | 934.18 | 444.76 | 991.85 | −6.29 | −68.37 | −42.99 | 0.94 | 0.45 | 1.28 |

1986–2017 | 927.89 | 376.39 | 948.86 | 0.98 | 0.40 | 1.45 |

### Driving factors cyclicality and correlation analysis

### Annual ecological flow process

*Q*

_{90}method were obtained, and the monthly ecological flow processes obtained by the two methods are shown in Figure 7. It can be seen from the monthly ecological flow process line shown in the figure that, except for May, the monthly ecological flow determined by the improved

*Q*

_{90}method is higher than that determined by the improved annual distribution method for all other months, and this phenomenon is more pronounced during the wet seasons.

Verified by Tennant's test (Table 4), there are 11 months within the optimal range in the improved *Q*_{90} method, which is 5 months more than the improved annual distribution method. It can be seen that the ecological flow defined by the improved *Q*_{90} method can better meet the water demand of the river ecosystem compared to the improved annual distribution method.

Month . | Percentage of monthly average flow for the improved annual distribution method (%) . | Flow description . | Percentage of monthly average flow for the improved Q_{90} method (%)
. | Flow description . |
---|---|---|---|---|

Jan | 62 | Optimal | 96 | Optimal |

Feb | 62 | Optimal | 96 | Optimal |

Mar | 62 | Optimal | 96 | Optimal |

Apr | 62 | Optimal | 96 | Optimal |

May | 43 | Good–Very Good | 43 | Good–Very Good |

Jun | 46 | Good–Very Good | 68 | Optimal |

Jul | 46 | Good–Very Good | 68 | Optimal |

Aug | 46 | Good–Very Good | 68 | Optimal |

Sep | 46 | Good–Very Good | 68 | Optimal |

Oct | 46 | Excellent–Optimal | 68 | Optimal |

Nov | 62 | Optimal | 96 | Optimal |

Dec | 62 | Optimal | 96 | Optimal |

Month . | Percentage of monthly average flow for the improved annual distribution method (%) . | Flow description . | Percentage of monthly average flow for the improved Q_{90} method (%)
. | Flow description . |
---|---|---|---|---|

Jan | 62 | Optimal | 96 | Optimal |

Feb | 62 | Optimal | 96 | Optimal |

Mar | 62 | Optimal | 96 | Optimal |

Apr | 62 | Optimal | 96 | Optimal |

May | 43 | Good–Very Good | 43 | Good–Very Good |

Jun | 46 | Good–Very Good | 68 | Optimal |

Jul | 46 | Good–Very Good | 68 | Optimal |

Aug | 46 | Good–Very Good | 68 | Optimal |

Sep | 46 | Good–Very Good | 68 | Optimal |

Oct | 46 | Excellent–Optimal | 68 | Optimal |

Nov | 62 | Optimal | 96 | Optimal |

Dec | 62 | Optimal | 96 | Optimal |

### Study on multi-scale ecological flow guarantee degree

## DISCUSSION

In the short term, it is typical to assume that the topography and soil conditions of the basin are largely stable and that changes in runoff are primarily caused by human activities and climate change. From the perspective of human activities, with the successive construction of cascade dams, the connectivity between the upstream and downstream of the dam is blocked, and the increase in the reservoir area leads to an increase in basin evaporation (Zhang *et al.* 2023b), resulting in a decreasing trend of annual runoff in the Jialing River basin, and Yang *et al.* (2023) have also demonstrated through research that the trend of river flow reduction in the future will further intensify. In addition, human activities can also affect the underlying surface of the basin. This study found that the rate of increase of parameter *n* of the underlying surface after 1986 has slowed down, indicating that measures such as soil and water conservation to change n have a stable impact on runoff, due to the limitation of the basin's land area. Richards & Belcher (2020) achieved similar research results in their study of global-scale underlying surfaces. From the perspective of climate change, compared with the base period, the aridity index Φ has increased and the *R*/*P* has decreased during the human activities period in the Jialing River basin, and the phenomenon of drought has intensified, which is consistent with the intensified drought trend in the Yangtze River basin in recent years. In the 21st century, the risk of drought will further increase with the rise of temperature, and it may be irreversible (Peterson *et al.* 2021). Ma *et al.* (2023) found that there is a tendency for drought spread to further intensify in six basins of the Yangtze River and that the extension of drought spread is shortened in most of the basins. Under the combined effects of drought and temperature rise, frequent ‘floating head death’ of fish is also caused, seriously threatening the health and safety of the river ecosystem (Ribeiro *et al.* 2022). Therefore, after determining the ecological flow of the river, it can be used as the minimum standard of flow, and then the guaranteed degree of runoff can be improved through ecological flow scheduling to mitigate climate drought and meet the survival needs of fish and other living organisms (Paudel *et al.* 2020). This is necessary for maintaining the balance of the ecosystem.

It was also found that the resonance period of the cross-wavelet transform and wavelet coherence spectrum of the underlying surface parameter concerning the average runoff depth of each year was less pronounced than that of the precipitation, potential evapotranspiration, and aridity index, and the periodicity concerning the precipitation and aridity index was the most pronounced (Sun *et al.* 2020). This is because, in general, precipitation and potential evapotranspiration are strongly distributed periodically by various meteorological factors, while significant changes in the underlying surface conditions of the basin are the result of long-term human activities (Zhao *et al.* 2023), so the periodicity is less pronounced, and it can also be seen that the runoff depth shows a significant negative correlation with the underlying surface parameter in each resonance cycle. In other words, the runoff depth of the watershed decreases with the increase of vegetation cover and other factors, which is consistent with the study of Wu *et al.* (2022) on runoff changes in the transition zone between Qinling and Loess Plateau.

In addition, compared to the base period, the growth rate of the guarantee degree in each year of the human activities period tends to accelerate further, but the fluctuation tends to be more moderate. Because the ecological flow delineated in this study can be considered as the minimum flow to maintain the health of the river ecosystem, its existence is meant to ensure that the river ecosystem will not suffer serious damage (Bestgen *et al.* 2020; Sofi *et al.* 2020). Under the multi-stage hydropower project in the Jialing River basin, the river flow is homogenized and the flow fluctuation is less, and most of the daily flow can meet the ecological flow demand, so the guarantee degree will be increased after the abrupt change year instead. For the dry seasons, from 1956 to 1985, the flow fluctuated more under the influence of the humid subtropical monsoon climate (Xiong *et al.* 2023), while after 1986, the flow fluctuation tended to level off, and the guarantee degree increased under the influence of the regulation of the stepped reservoir group. However, the guarantee degree during the normal and wet seasons after the abrupt change year was lower than that before the abrupt change year, indicating that the graded reservoirs of the Jialing River did not contribute much to improving the ecological flow guarantee degree during the normal and wet seasons (Han *et al.* 2022). From the monthly point of view, except for June, the guaranteed degree of the wet seasons is lower than that after the abrupt change year. The low guaranteed degree of the ecological flow during the wet seasons should attract the attention of the basin decision-makers, and the flow demand of the ecosystem during this period should be maintained as much as possible to keep the river ecosystem healthy based on the river flood control project (Diehl *et al.* 2020).

## CONCLUSION

In this study, the degree of change in the hydrological regime of the Jialing River basin was assessed with the help of the IHA-RVA and RI assessment frameworks, the contribution of the drivers to the change in runoff volume was quantified through the Budyko theory, and the periodicity and correlation of the drivers with the depth of runoff were also analyzed. In addition, a comprehensive assessment of the guarantee degree in terms of inter-annual and intra-annual variability characteristics was carried out in this study, and the conclusions were as follows:

It was found that the annual runoff volume of the Jialing River from 1956 to 2018 showed a significant decreasing trend, and the hydrological regime of the Jialing River basin before and after the abrupt change year was moderately changed. Among the factors affecting the runoff volume, the contribution of human activities to the change in runoff was the largest (54.68%), followed by precipitation (46.07%), while evapotranspiration had the least effect (−3.00%). The human activity changes in runoff were mainly reflected in the changes in the underlying surface conditions of the basin, but the changes in the underlying surface conditions slowed down after the abrupt change due to the size of the basin. The ecological flow delineated by the improved *Q*_{90} method was in the optimal range for 11 months, which could better meet the flow demand of the Jialing River basin ecosystem. In addition, the growth rate of the guarantee degree in each year of the human activities period tends to accelerate further, but the fluctuation tends to be more gentle after 2010. After the abrupt change year, the guarantee degree is higher than that before the abrupt change year in the dry seasons, but lower than that before the the abrupt change year in the flat seasons and the normal seasons. On a monthly scale, the guarantee degree of the human activities period from December to March is higher than that of the base period, while the guarantee degree of the human activities period from April to November (except June) is lower than that of the base period.

The results of this study provide valuable information for the sustainable utilization of water resources in the Jialing River basin and help to provide a reference for decision-makers to cope with the changes in the water environment of the Jialing River basin. Further research should focus on specifically analyzing the impacts of different types of human activities on runoff changes and assessing in detail the impacts of changes in the hydrological regime on aquatic organisms such as fish.

## ETHICAL APPROVAL

Not required as the study did not involve humans or animals.

## CONSENT TO PARTICIPATE

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## CONSENT TO PUBLISH

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## AUTHOR CONTRIBUTIONS

W.G. rendered support in funding acquisition, project administration, arranging the resources, investigating the data, and supervising the work; G.W. conceptualized the whole article, conducted data curation, rendered support in formal analysis, investigated the data, developed the methodology, brought the resources; arranged the software, validated the data; visualized the project, wrote the original draft and reviewed & edited the article; Y.M., F.H., L.H., H.Y., X.J., and N.H. investigated the data, rendered support in formal analysis, developed the methodology, validated the data, and visualized the work. H.W. rendered support in funding acquisition and project administration.

## FUNDING

This study was supported by the National Natural Science Fund of China (51779094) and the Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (23ZX012).

## 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.