Abstract
River hydrology is an important proxy for changes in river runoff and an important factor affecting the ecology of rivers. To quantitatively evaluate the hydrology of the Wuijang River basin, this paper uses various tests to analyze runoff. The IHA-RVA method combined with FDC ecohydrological indicators was used to evaluate the hydrology of the Wuijang River basin and to analyze and calculate the contribution of human activities and climate change to runoff. The results show that (1) the runoff in the Wujiang River basin has shown a decreasing trend over the years, with a sudden change in 2005 and mainly two inter-annual cycles; (2) the overall hydrological change in runoff is 48%, which is a moderate change; (3) The changes in FDC ecological indicators are significantly correlated with rainfall, and the correlation between FDC ecological indicators and IHA hydrological indicators is strong; (4) human activities are the main influencing factors of runoff changes in the Wujiang River. The results of this paper have some reference value for the management of the Wujiang River basin and the improvement and restoration of river ecology.
HIGHLIGHTS
Analysis of changes and trends in the hydrological situation of the Wujiang River basin over the past 60 years.
Analysis of the degree of change in the overall hydrological situation of the Wujiang River basin.
Comparison of results of hydrological situation analysis under multiple hydrological indicators.
Analysis of the contribution of climate change and human activities to hydrological change in the Wujiang River.
INTRODUCTION
Natural water systems such as rivers and lakes have always played a very active role in maintaining the ecological balance, ensuring the prosperity of organisms in the water system and the economic development of human society (Chenning et al. 2021). This is why changes in the hydrological conditions of rivers and lakes are so important to their ecological balance. Changes in river hydrology, therefore, have a direct impact on the ecological integrity of rivers, the biodiversity of the region, and the activities of human society (Dai et al. 2019; Guo et al. 2019). With the development of human society, there is a growing trend toward the use of hydrological models. At the same time, a growing body of literature and research shows that the occurrence of climate extremes and progressively more frequent human activities can cause significant changes in the hydrological characteristics of rivers and even threaten their ecosystem functions as a result (Guo et al. 2018; Kunlong et al. 2021). The causes of hydrological changes in water systems such as rivers and lakes can generally be summarized as climate change and the construction of hydraulic structures by humans (Cheng et al. 2019; Shuhui et al. 2021). In general, the flow of rivers is initially influenced mainly by precipitation during natural periods, i.e. climate change, but as water projects are built and operated on the river, the driving force of precipitation diminishes and the influence of hydraulic structures such as hydroelectric power stations and reservoirs on the river becomes prominent (Maviza & Ahmed 2021; van Rooyen et al. 2021). Understanding the changes in river flow regimes and analyzing the main factors that cause changes in river conditions is important for improving river ecology and establishing a rational regime.
So far, more than 170 indicator methods have been used in hydrological situation-related research (Tao et al. 2017; Yongwei et al. 2021; Yan et al. 2022), among which the most accepted and used are the hydrological indicator method (IHA) and the derived range of variation method (RVA) proposed by Richter et al. (1996). The IHA-RVA method is an effective analytical tool for assessing the impact of human activities on river runoff processes through the establishment of a system of indicators, and several scholars at home and abroad have conducted research on hydrological changes based on this method and achieved good results (Wang et al. 2019; Gierszewski et al. 2020; Li & Qi 2021; Yongwei et al. 2021; Zeng et al. 2021; Guo et al. 2022a, 2022b). However, these hydrological indicators do not reflect the impact of human activities on river runoff processes, nor do they reflect the specific changes in river ecological flows, which often directly indicate the characteristics of river ecosystems (Vogel et al. 2007; Gao et al. 2009, 2012; Palmer & Ruhi 2019). To better reflect ecological changes in rivers, Vogel et al. (2007) introduced flow duration curve (FDC)-based indicators of ecological deficit and ecological surplus. These indicators represent flow deficits or surpluses due to flow variability and directly reflect the overall loss or gain in in-stream demand. Gao et al. (2012) further improved these indicators by setting 25 and 75% of the FDC indicators as the upper limit of ecological deficit and the lower limit of ecological surplus, respectively, based on the FDC. The FDC ecological indicators can use ecological deficit and ecological surplus indicators to respond to the ecological regime of the river at multiple time scales, and some studies have shown that the method creates few indicators, is dimensionless, and is a good overall representation of the degree of change in runoff time series (Palmer & Ruhi 2019; Yumeng et al. 2021). Zhao et al. (2020) evaluated the impact of the upstream terrace power station in Panzhihua on the hydrological situation of the station based on the FDC ecological indicators. Gu et al. (2016) evaluated the hydrology of rivers in the Dongjiang basin based on multiple hydrological change indicators. However, in the current studies on the hydrological situation of the upper Yangtze River basin, there are relatively few studies that simultaneously apply multiple hydrological indicators such as FDC ecological indicators for hydrological situation analysis.
In this study, the runoff data from the Wulong hydrological station of the Wujiang River from 1956 to 2019 and rainfall data from several hydrological stations were used to analyze the sudden variability and trend changes of runoff using the MK mutation test combined with the sliding T and cumulative distance level test and then analyzed the periodicity and inter-annual variability of flow using wavelet functions. The hydrological index method (IHA) and the range of variation method (RVA) were used to quantitatively evaluate the variability of the hydrological index at the Wulong Station, while the FDC-based ecohydrological index method was used to evaluate and analyze the hydrological variability at the Wulong Station from annual and seasonal scales.
RESEARCH MATERIALS AND METHODS
Study hydrographic stations and data sources
Topographic map of Wujiang River Basin.
The hydrological data used in this study are daily runoff data from the Wulong hydrological station from 1956 to 2019 and daily precipitation data from 11 hydrological stations in the Qianjiang, Weaving, and Xishui regions of the Wujiang River basin for the same years, combining the two types of data to study and analyze the changes in the hydrological situation of the Wujiang River for more than 60 years. The runoff data used in the study were obtained from the Yangtze River Basin Hydrological Yearbook, and the rainfall data were downloaded from the China Meteorological Data Website (http://data.cma.cn/).
Research methodology
Trend analysis, mutation test, and periodicity analysis
This paper uses the Mann-Kendall non-parametric test (Sharma et al. 2019; Eccles et al. 2020) to determine the abrupt change point of the runoff series and tests whether the inter-annual variation trend of hydrological elements is significant by comparing the critical value of the statistic Z with the confidence level α, and also combines the cumulative distance level method (He et al. 2013; Li et al. 2016) and sliding t-test (Zhu et al. 2011; Fu et al. 2018). The analysis of the inter-annual variability of hydrological elements is carried out by comparing the statistical value Z with the critical value of the confidence level α. In the analysis of the periodicity of runoff, the complex Morlet wavelet function (Guo et al. 2022a, 2022b) was used to explore the inter-annual variability of the hydrological series at different time scales. The analysis explores the variability of the runoff cycle at different time scales.
Hydrological indicator range of variation method IHA-RVA
To quantify the degree of variability in river hydrological conditions, the range of variation (RVA) method proposed by Richter et al. was applied, which is based on the index of hydrological variability (IHA) (Black et al. 2005; Richter 2005; Table 1). The 33 ecohydrological indicators were developed to assess the hydrological situation of rivers subject to external influences in five groups: flow, degree, events, frequency, and rate of change.
IHA hydrological indicators
| IHA parameter group | Parameter characteristics | Hydrological parameters |
|---|---|---|
| Magnitude of monthly water conditions | Degree, time | Median value for each calendar month |
| Magnitude and duration of annual extreme water condition | Degree, duration | Annual minima 1-day means Annual maxima 1-day means Annual minima 3-day means Annual maxima 3-day means Annual minima 7-day means Annual maxima 7-day means Annual minima 30-day means Annual maxima 30-day means Annual minima 90-day means Annual maxima 90-day means Number of zero days Base flow index |
| Timing of annual extreme water conditions | Time of appearance | Date of minimum Date of maximum |
| Frequency and duration of high and low pulses | Degree, frequency, duration | Number of high pulse each year Number of low pulse each year Mean duration of high pulses within each year Mean duration of low pulses within each year |
| Rate and frequency of water condition changes | Frequency, flood rise, and fall ratio | Rise rate Fall rate Number of reversals |
| IHA parameter group | Parameter characteristics | Hydrological parameters |
|---|---|---|
| Magnitude of monthly water conditions | Degree, time | Median value for each calendar month |
| Magnitude and duration of annual extreme water condition | Degree, duration | Annual minima 1-day means Annual maxima 1-day means Annual minima 3-day means Annual maxima 3-day means Annual minima 7-day means Annual maxima 7-day means Annual minima 30-day means Annual maxima 30-day means Annual minima 90-day means Annual maxima 90-day means Number of zero days Base flow index |
| Timing of annual extreme water conditions | Time of appearance | Date of minimum Date of maximum |
| Frequency and duration of high and low pulses | Degree, frequency, duration | Number of high pulse each year Number of low pulse each year Mean duration of high pulses within each year Mean duration of low pulses within each year |
| Rate and frequency of water condition changes | Frequency, flood rise, and fall ratio | Rise rate Fall rate Number of reversals |
It also states that Do values less than 33% are considered unaltered or low alteration; between 33 and 67% are considered a moderate alteration, and 67 and 100% are considered a high alteration.
FDC ecohydrological indicator method
Biodiversity impact assessment
Equations (2)–(5) have been used in China to respond to river and lake biodiversity, with good results (Gu et al. 2016; Wang et al. 2022). Due to the lack of direct data on riverine biomes and species in the Wujiang River in this study, it was not possible to directly calculate the SI index using Equations (2)–(4). In this paper, we use the Wujiang River hydrological data to roughly calculate the riverine biodiversity using Equations (2)–(5).
Cumulative volume slope rate of change method
The cumulative slope rate of change method was proposed by Wang et al. (2013). This method is proposed to quantitatively assess the contribution of human activities and climate change to river runoff. The method assumes that if the annual change in sand transport is influenced only by precipitation, then the slope of the cumulative curve of precipitation and sand transport over time varies in the same ratio. The combination of all influencing factors of the variable is defined as 1, and the degree of influence of each influencing factor on the variable is deduced from the ratio of the cumulative slope of the various influencing factors over time to the rate of change of the cumulative slope of the variable. The slope of the variable before and after the inflection point is YRa and YRb, respectively, and the slope of cumulative precipitation before and after the inflection point is YPa and YPb, respectively.
ANALYSIS OF RESULTS
Flow evolutionary characteristics
Flowchart for working procedures and employed methods in the study.
Characteristics of intra-annual distribution of runoff and inter-annual variation of runoff at the Wulong Station.
Characteristics of intra-annual distribution of runoff and inter-annual variation of runoff at the Wulong Station.
Figure 3(b) shows the annual runoff flow variation curve of the Wujiang River of the Wulong Station from 1956 to 2019, a total of 64 years. Its multi-year average runoff flow is 4.82 × 1010 m3/s, with the maximum runoff flow in 1977, 6.86 × 1010 m3/s, and the minimum value in 2006, 2.89 × 1010 m3/s. The average annual flow at the hydrological station is generally decreasing, but this trend is not significant.
Mutation test curve (a) MK mutation test curve, (b) sliding T mutation test curve, and (c) cumulative distance level test curve. The years marked in the figure are those that passed the significance test and may have mutations.
Mutation test curve (a) MK mutation test curve, (b) sliding T mutation test curve, and (c) cumulative distance level test curve. The years marked in the figure are those that passed the significance test and may have mutations.
Wavelet analysis of annual mean flow at the Wulong Station. (a) A contour plot, where brown represents years of water deficit and cyan represents years of abundance, with shades of color representing the level of water deficit and abundance; (b) A wavelet variance plot, where each peak represents a possible cycle. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Wavelet analysis of annual mean flow at the Wulong Station. (a) A contour plot, where brown represents years of water deficit and cyan represents years of abundance, with shades of color representing the level of water deficit and abundance; (b) A wavelet variance plot, where each peak represents a possible cycle. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Quantitative assessment of the hydrological situation
To quantitatively reveal the degree of hydrological variability at the Wulong Station in the past six decades, the runoff series were divided into two time periods, 1956–2004 and 2005–2019, based on the results of the abrupt change test. The hydrological indicator method and the range of variation method (IHA-RVA) were used to analyze and calculate the changes in hydrological indicators. Since the Wulong Station has never been disconnected during the years of the series data used, the remaining 32 hydrological indicators, except for this disconnection, were selected as the target of the calculation. The 32 hydrological indicators were divided into five groups: monthly median flows, annual extreme flows, annual extreme flow occurrence times, high and low flow frequencies and ephemerides, and flow variability and frequency. The degree of change in hydrological indicators was calculated for each item, group, and overall, and the results are shown in Tables 2 and 3 (between 0 and 33% is no change or low change; between 33 and 67% is moderate change; between 67 and 100% is high change).
Calculation results of ecohydrological indicators of the Wulong hydrological station
| Hydrological indicators | Before the mutation | After the mutation | Degree of change (%) | Hydrological indicators | Before the mutation | After the mutation | Degree of change (%) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1 group | Median January | 436 | 644 | −31 | 2 groups | Annual average 90-day minimum | 437.1 | 653.2 | −48 |
| February median | 418 | 576.3 | −83 | Annual average 1-day maximum | 11,900 | 8,405 | −31 | ||
| Median March | 506 | 773.5 | −65 | Annual average 3-day maximum | 10,270 | 6,503 | −31 | ||
| April median | 993.5 | 1,198 | −13 | Annual average 7-day maximum | 8,146 | 5,321 | −31 | ||
| Median May | 2,000 | 1,895 | −13 | Annual average 30-day maximum | 4,526 | 3,568 | −48 | ||
| June median | 2,503 | 2,243 | 56 | Annual average 90-day maximum | 3,314 | 2,612 | −48 | ||
| Median July | 2,405 | 2,380 | 64 | Baseflow Index | 0.219 | 0.2809 | −31 | ||
| August median | 1,395 | 1,305 | 22 | 3 groups | Time of occurrence of the annual minimum | 45 | 43 | −48 | |
| Median September | 1,218 | 1,323 | −34 | Time of occurrence of annual maximum | 182 | 195.5 | 4 | ||
| Median October | 1,155 | 883.5 | 39 | 4 groups | Low pulse count | 5 | 15 | 87 | |
| Median November | 828.5 | 736.3 | 39 | Low pulse duration | 6 | 2 | −85 | ||
| Median December | 533 | 612 | −48 | High pulse count | 11 | 10 | −28 | ||
| Annual average 1-day minimum | 300.5 | 336.5 | 9 | High pulse duration | 4.5 | 3 | −22 | ||
| Annual average 3-day minimum | 304.5 | 373.5 | −13 | 5 groups | Rate of increase | 89.5 | 119 | −31 | |
| Annual average 7-day minimum | 326.4 | 407.9 | −13 | Decline rate | −70 | −121 | −69 | ||
| Annual average 30-day minimum | 368.9 | 504.3 | −48 | Number of reversals | 114 | 179 | −100 |
| Hydrological indicators | Before the mutation | After the mutation | Degree of change (%) | Hydrological indicators | Before the mutation | After the mutation | Degree of change (%) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1 group | Median January | 436 | 644 | −31 | 2 groups | Annual average 90-day minimum | 437.1 | 653.2 | −48 |
| February median | 418 | 576.3 | −83 | Annual average 1-day maximum | 11,900 | 8,405 | −31 | ||
| Median March | 506 | 773.5 | −65 | Annual average 3-day maximum | 10,270 | 6,503 | −31 | ||
| April median | 993.5 | 1,198 | −13 | Annual average 7-day maximum | 8,146 | 5,321 | −31 | ||
| Median May | 2,000 | 1,895 | −13 | Annual average 30-day maximum | 4,526 | 3,568 | −48 | ||
| June median | 2,503 | 2,243 | 56 | Annual average 90-day maximum | 3,314 | 2,612 | −48 | ||
| Median July | 2,405 | 2,380 | 64 | Baseflow Index | 0.219 | 0.2809 | −31 | ||
| August median | 1,395 | 1,305 | 22 | 3 groups | Time of occurrence of the annual minimum | 45 | 43 | −48 | |
| Median September | 1,218 | 1,323 | −34 | Time of occurrence of annual maximum | 182 | 195.5 | 4 | ||
| Median October | 1,155 | 883.5 | 39 | 4 groups | Low pulse count | 5 | 15 | 87 | |
| Median November | 828.5 | 736.3 | 39 | Low pulse duration | 6 | 2 | −85 | ||
| Median December | 533 | 612 | −48 | High pulse count | 11 | 10 | −28 | ||
| Annual average 1-day minimum | 300.5 | 336.5 | 9 | High pulse duration | 4.5 | 3 | −22 | ||
| Annual average 3-day minimum | 304.5 | 373.5 | −13 | 5 groups | Rate of increase | 89.5 | 119 | −31 | |
| Annual average 7-day minimum | 326.4 | 407.9 | −13 | Decline rate | −70 | −121 | −69 | ||
| Annual average 30-day minimum | 368.9 | 504.3 | −48 | Number of reversals | 114 | 179 | −100 |
Overall hydrological alteration degree
| Hydrological station | Hydrological alteration (%) | Overall hydrological alteration (%) | ||||
|---|---|---|---|---|---|---|
| Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | ||
| Wulong | 47 | 33 | 34 | 63 | 72 | 48 |
| Hydrological station | Hydrological alteration (%) | Overall hydrological alteration (%) | ||||
|---|---|---|---|---|---|---|
| Group 1 | Group 2 | Group 3 | Group 4 | Group 5 | ||
| Wulong | 47 | 33 | 34 | 63 | 72 | 48 |
According to the calculations shown, the overall degree of change for the 32 IHA hydrological indicators was 48%, reaching a moderate degree of change. Groups 1, 2, 3, and 4 have an overall degree of change of 47, 33, 34, and 63%, respectively, which is moderate. In group 5, the rate of change of flow and frequency reached 72%, which is a high degree of alteration. Although the overall change in hydrological indicators in group 2 was not significant after the mutation, the annual mean extreme flows were almost completely reduced compared to those before the mutation, especially the annual mean maximum flows were reduced more significantly. It can therefore be assumed that the abrupt change has had a negative impact on runoff.
Wulong Station hydrological index change degree.
Changes in ecological runoff indicators
Flow duration curves (FDCs) before (green curve) and after (pink curve) the onset of the mutation. The two black curves in the graph show the 75% percentile (upper limit) and the 25% percentile (lower limit). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Flow duration curves (FDCs) before (green curve) and after (pink curve) the onset of the mutation. The two black curves in the graph show the 75% percentile (upper limit) and the 25% percentile (lower limit). Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Annual and seasonal scale precipitation spacing and changes in ecological indicators. The blue bar represents the precipitation distance level, the purple dash represents the ecological surplus, and the red dash represents the ecological deficit. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Annual and seasonal scale precipitation spacing and changes in ecological indicators. The blue bar represents the precipitation distance level, the purple dash represents the ecological surplus, and the red dash represents the ecological deficit. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
From Figure 8(b)–8(e), it can be seen that the correlation between rainfall spacing and ecological indicators varies considerably at different seasonal scales. Among the seasons, the correlation between ecological indicators and precipitation spacing is most significant in summer and is most similar to the trend of ecological indicators and rainfall spacing at the annual scale; among the other seasons, the correlation between ecological indicators and precipitation spacing is weakest in winter and also differs most from the annual scale graph; the correlation between precipitation spacing and ecological indicators in spring and autumn is between summer and winter. This phenomenon can be attributed to the fact that rainfall in the region is mainly concentrated in summer (as shown in Figure 2), and that when precipitation is high enough, it can be the main factor in changing regional flows. So it can also be assumed that the variability of rainfall and ecological indicators in summer best reflects the changing characteristics of the year.
Box plot of inter-annual variation of annual and quarterly FDC ecological indicators.
Box plot of inter-annual variation of annual and quarterly FDC ecological indicators.
Ecological response assessment
The time-varying characteristics of total season eco-surplus and eco-deficit.
A linear fit analysis shows that the trends in the ecological surplus and deficit are more or less the same and have remained relatively stable over the years. Overall, most of the variation in the ecological surplus and deficit indicators is concentrated in the range of 0–0.5. In Figure 10 alone, there is no significant change in the total seasonal ecological indicators over the years of sudden change.
The time-varying characteristics of biodiversity indicators SI.
Correlation between IHA indicators and FDC ecological indicators
Both the IHA hydrological indicator and the FDC ecological indicator are valid methods for studying hydrological conditions. In this paper, both indicators are based on more than 60 years of runoff data and are essentially a response to the hydrological variability of runoff in the basin. The focus of the two indicators differs in that the ecological surplus and ecological deficit are used to analyze annual flow variability and to characterize seasonal changes in flow patterns, while the IHA indicator is used to analyze detailed changes in flow, thus providing more detailed information on flow patterns. The combination of the ecological flow indicator and the IHA indicator can provide a powerful method for measuring the degree of variability in flow regimes, so a focus on the correlation between the two is more helpful in illustrating the plausibility and accuracy of studies in hydrological change.
Correlation coefficients between ecological runoff indicators and IHA 32 hydrological indicators. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
Correlation coefficients between ecological runoff indicators and IHA 32 hydrological indicators. Please refer to the online version of this paper to see this figure in colour: https://dx.doi.org/10.2166/nh.2023.004.
The IHA indicators BFI (baseflow index), LPC (low pulse count), and FR (rate of decline) show significant negative correlations with most of the ecological runoff indicators, while Dmax (time to annual maximum) and Dmin (time to an annual minimum) show weak or no correlation with the ecological indicators. Dmax (time to an annual maximum) and Dmin (time to an annual minimum) were weakly or even not correlated with the ecological indicators. The FDC ecological indicators (ecological surplus and deficit) can reflect the changes in runoff from one season to another and the information of the IHA indicators. In contrast to the IHA indicator method, which has a large number of indicators, the FDC ecological indicators provide a visual representation of the hydrological situation.
Contribution of climate change and human impacts on runoff
As mentioned above, there is a certain trend in runoff from the Wulong hydrological station, and although the changes are not significant, they are in an overall declining state. The study of the main factors influencing the changes in river runoff and the causes of their effects is also an important part of the quantitative evaluation of the hydrological situation of the river. The main factors influencing changes in river runoff and causes of their effects are also an important part of a quantitative assessment of river hydrology. The main factors affecting the change of river runoff can be summarized as climatic factors and human activities. In this paper, the cumulative slope rate of change method is used to calculate the contribution of the two types of factors and analyze their causes.
The results calculated by the cumulative slope rate of change method are shown in Table 4. It can be seen that after the abrupt change, i.e. the Tb period, the results of the rainfall contribution to runoff calculation show that human activities are the main factor affecting the change in runoff in the Wujiang River compared to factors such as precipitation and climate change.
Contribution of climate change and human activities to changes in water and sediment
| Periods | Contribution of precipitation to runoff ( ) | Contribution of human activities to runoff ( ) |
|---|---|---|
 | – | – |
 | 36.28% | 63.72% |
| Periods | Contribution of precipitation to runoff () | Contribution of human activities to runoff () |
|---|---|---|
| | – | – |
| | 36.28% | 63.72% |
DISCUSSION
Impact of climate change on runoff
Hydrological conditions are an important part of research in river management and development. In this study, hydrological methods were used to quantify the changes in the hydrological conditions of the Wujiang River, as well as to quantify the effects of climate change and human activities on the changes in the runoff of the river. The following conclusions were drawn: the runoff of the River has been decreasing over the past 60 years and the hydrological situation has changed significantly. The main causes of changes in hydrological conditions, in general, include climatic factors and the influence of human activities. Among these, climate change may directly affect evapotranspiration, precipitation, and plant use efficiency, which in turn affects changes in hydrological systems such as basin runoff and flooding (Guo et al. 2022c). A study by Xie et al. (2020) showed that since 2003, temperatures in the upper Yangtze River have increased significantly at a rate of 0.16 °C per decade, resulting in more evapotranspiration in the basin, as well as a reduction in the duration and increase in the intensity of the precipitation.
Rainfall at the Wulong hydrological station of the Wujiang River – flow relationships and multi-year rainfall trends.
Rainfall at the Wulong hydrological station of the Wujiang River – flow relationships and multi-year rainfall trends.
Impact of human activities on runoff
Anthropogenic activities such as the development of the Wujiang hydroelectric project have also caused changes in the original hydrological situation, with 11 hydropower stations having been built on the main stem of the Wujiang River and its major tributaries from 1982 to 2013 (Guo et al. 2021). According to the calculation of the contribution rate, human activities such as the construction of hydroelectric projects are the main cause of the changes in the hydrological situation of the Wujiang River. For example, the Pengshui Hydropower Station, located near the Wulong hydrological station, was completed in 2009 and started to store water. The operation of the Pengshui Hydropower Station, which is the largest peak system daily regulating hydropower project on the mainstream of the Wujiang River, is bound to change its original hydrological situation, resulting in differences in flow upstream and downstream of the station.
After all, large-scale development and soil and water conservation measures take years to complete, and their impact on the flow of the river basin is gradually increasing or decreasing, showing a gradual change (Yang et al. 2018; Yu et al. 2021). Between 1984 and 2017, the Wujiang River has seen the construction of the Wujiangdu Hydropower Station (1982), the Pudding Hydropower Station (1995), the Dongfeng Hydropower Station (1995), the Yinzidu Hydropower Station (2003), the Hongjiadu Hydropower Station (2004), and the Suofengying Hydropower Station (2005). Relevant studies have shown that the completion and operation of these large hydropower hubs not only affect the inter-annual distribution of runoff but also have an impact on the flow of the river. The studies have shown that the construction and operation of these large hydropower hubs not only affect the inter-annual distribution of runoff but also have a significant impact on the amount of sand transported by the rivers, which in turn changes the lower bedding surface of the rivers and has a longer-term impact on runoff (Guo et al. 2021).
Changes in the hydrological situation of the Wujiang River inevitably affect the ecology of the river basin, and this study also aims to investigate the ecological response of the river to changes in the runoff of the Wujiang River. Changes in the biodiversity index (SI) show a significant decline in biodiversity in the Wujiang River basin over the years. The most obvious manifestation of this is the decrease in fish stocks due to the river's reduction in flow and hydrological conditions.
Since the construction and operation of the various hydropower stations on the Wu River began in 1982, the hydrological situation of the river has been altered to a large extent, and this has inevitably had an impact on the survival and reproduction of fish. The impact on the fish habitat has been cumulative, with the spawning of drifting fish declining significantly by 2011 and the fish themselves adapting to the dramatic changes in habitat after 2014, increasing the number of fish spawning (Gu et al. 2022). Due to the top-supporting effect between the terrace reservoirs, the original river is easily converted from river phase to lake phase, and the flow velocity in the reservoir slows down compared to the natural river, but some fish adapted to fast-flowing habitats such as white snapper and round-mouthed copperhead and drifting fish such as longfin minnow, cylindrical minnow, and Chinese golden sand loach (Xu et al. 2019). The slowing of the river flow will affect the normal spawning of these fish, which need to spawn at a certain flow rate. In addition, the construction of hydraulic projects also hinders the normal functioning of fish migration channels in rivers (Zhang et al. 2018). In conclusion, human activities have had a significant impact not only on the hydrological situation of the river but also on its biodiversity and habitat.
CONCLUSION
- (1)
The runoff of the Wujiang River basin has shown an overall non-significant downward trend over the past 60 years, and the sudden annual change in the sequence was judged to have occurred in 2005 by combining various tests. The results of wavelet function analysis show that there are two major cyclical changes in flow at the Wulong Station, from 14–18 years and 24–30 years. The calculation results of the cumulative slope rate of change method show that the contribution of precipitation to runoff is 36.28% and the contribution of human activities to precipitation is 63.72%.
- (2)
IHA-RVA method of the 32 hydrological indicators used, 16% were height alteration indicators, 37% were moderate alteration indicators, and 47% were low alteration indicators. The overall hydrological alteration of runoff at the Wulong Station is 48%, which is moderate.
- (3)
Through the evaluation based on the FDC ecological indicators, the ecological indicators in the region changed significantly after the mutation, especially the ecological deficit increased significantly, and the summer indicators showed the most consistent changes with the overall change pattern, and the precipitation distance level was strongly correlated with the FDC ecological indicators. The results of the correlation analysis show that the FDC ecological indicators reflect the information on the performance of the IHA hydrological indicators very well.
- (4)
According to the analysis of the change characteristics of the SI index over time, the biodiversity of the Wujiang River basin had already started to decline before the sudden change occurred, and the ecological quality of the river basin has been decreasing year by year for the past 60 years or so. The combined contribution analysis and discussion show that both climate change and human activities affect runoff and river ecology, with human activities being the most significant influencing factor.
DATA AVAILABILITY STATEMENT
All relevant data are available from an online repository or repositories (http://data.cma.cn/).
CONFLICT OF INTEREST
The authors declare there is no conflict.
