A Framework for Characterizing Spatio-Temporal Variation of Turbidity and Drivers in the Navigable and Turbid River: A Case Study of Xitiaoxi River

Abstract

Turbidity, as a key indicator of water quality linked to underwater light attenuation, is crucial for evaluating water quality. Control in high-turbidity water environments plays a critical role in navigable rivers. For this purpose, our study proposed a framework for analyzing the spatio-temporal variation of turbidity and its driving factors in a navigable and turbid river using in situ measurement data, satellite data, socioeconomic data, a power index function model, and correlation analysis. The results show that the proposed model is feasible for quantitative turbidity monitoring of the Xitiaoxi River. Its upstream turbidity is lower than downstream, with seasonal averages for spring, summer, autumn, and winter of 93.9, 111.3, 113.5, and 120.9 NTU, respectively. Furthermore, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005 and began to decline after 2005 due to the policy of mining moratorium. This trend is especially noticeable at monitoring points along the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and Huxi River (S8). Mining and shipping have significantly contributed to the turbidity of the target river. This framework offers a practical approach for assessing the environmental impacts of both natural and anthropogenic factors, thereby providing valuable insights for river management practices.

1. Introduction

Turbidity is an optical phenomenon that results from the scattering and absorption of light as it passes through a water column. The presence of particles in the water causes attenuation of incident light, with the extent of attenuation serving as a measure of turbidity [1]. Additionally, turbidity plays a significant role in aquatic ecosystems by influencing the absorption and distribution of solar energy, levels of biological activity, water body aesthetics, and holds particular importance in the study of marine ecology, biochemical processes, hydrodynamic environments, and material transport [2,3,4,5].

Water quality from inland water body monitoring traditionally relies on field sampling analysis [6]. However, this method is only suitable for low-frequency and small-scale data collection, demanding significant manpower and financial resources. Turbidity exhibits specific temporal and spatial distributions with pronounced seasonal variations, challenging routine sampling and laboratory analysis to accurately capture its fluctuations [7]. In recent decades, satellite remote sensing technology has been increasingly employed for monitoring inland water quality [8,9]. Although the study of turbidity retrieval via remote sensing has emerged relatively late compared to other water quality parameters, notable progress has been made. Utilizing multiple data sources including MODIS, LANDSAT, and ERS-2 SAR, researchers have conducted remote sensing studies on water turbidity in various regions such as Finland Lake [10], Gulf of Finland [11], Adour estuary of France [12], and DOS River basin of Brazil [13]. Results have demonstrated the effectiveness of the models and the reasonableness of inversion accuracy. Moreover, domestic researchers have developed turbidity inversion models for various regions in China, including Chagan Lake [14], Guanting Reservoir [15], Beibu Gulf [16], Pearl River Estuary [17], the main stream of Han River [18], East China Sea [1], Taihu Lake Basin [19], along with the Yangtze River Estuary and East China Sea [20], utilizing hyperspectral, Landsat, GOCI, and other satellite data. These studies have enabled the analysis of turbidity spatial distribution in the respective study areas based on inversion results.

Both domestic and foreign scholars have developed numerous models to estimate turbidity across various sea areas and large lakes at present [5,21,22,23]. These models encompass statistical, semi-analytical, physical, and specialized inversion approaches, such as neural networks [24,25,26,27]. Among them, the semi-empirical/semi-analytical method is widely utilized. This method establishes an inversion model based on the known spectral characteristics of water quality parameters, utilizing the statistical relationship between the optimal band combination and ground-measured values [28]. For instance, Cao [29] employed a band combination model to quantitatively monitor total suspended matter concentration and turbidity in Weishan Lake, analyzing spatio-temporal variations. Song [19] conducted quantitative inversion studies on cyanobacteria density and turbidity in Taihu Lake using Landsat 8 OLI data, achieving continuous improvement in inversion accuracy. Feng [18] developed a mathematical model for remote sensing inversion of turbidity in the middle and lower reaches of the Han River based on Landsat 8 OLI, effectively monitoring turbidity distribution in this region.

In areas rich in mineral resources, navigable rivers provide important shipping channels for regional economic development. However, this also led to a series of problems such as water turbidity, which led to the ecological degradation of the riverbank, the deterioration of water quality, the deterioration of water ecological environment quality, and other problems, such as in the Xitiaoxi River which is a crucial navigable waterway in Taihu Lake Basin. Yan [30] proposed a new turbidity source analysis framework including in situ sampling, vessel monitoring, remote sensing, and hydrodynamic modelling, where the critical processes of flow reversal from the turbid lake, navigation, and urban non-point source in urban lakeside river networks were explicitly considered during the period of investigation. Nonetheless, there lacks a systematic framework for investigating the temporal and spatial fluctuations in turbidity in past decades and specific drivers in high-turbidity navigable rivers. As a result, the temporal and spatial changes in rivers in such areas and the reasons for the changes are unclear, which limits the water ecological and timely management in similar areas. This study proposes a research framework aimed at developing a turbidity inversion model for the Xitiaoxi River using Landsat satellite data. It entails analyzing the temporal and spatial patterns of turbidity over the past 40 years and discussing the factors influencing turbidity in the Xitiaoxi River.

2. Study Area

Located in the upper reaches of the Taihu Lake Basin (Figure 1), the Xitiaoxi River serves as a vital water source for Taihu Lake. Originating from the north slope of Tianmu Mountain, it traverses through mountainous terrain characterized by high peaks and deep valleys before coursing southwest to northeast through Anji County. The river spans a total length of 145 km, with a basin area covering 2274 km². The gradient of the river varies from steep to gradual as it flows from Ancheng to Meixi, marking a transitional zone. Beyond Meixi, the channel slope gradually decreases, indicating a transition to a plain river. The natural difference in elevation along the river is of 297 m, with an annual runoff of 2.26 × 10⁸ m³.

The Xitiaoxi River Basin experiences a subtropical monsoon climate, with vegetation in Anji County classified under the subtropical eastern evergreen broad-leaved forest subzone and subtropical evergreen broad-leaved forest northern subzone. Rainfall predominantly occurs from April to September, with annual precipitation gradually decreasing from the southwest mountainous region to the northeast plain area. Annual runoff variation is basically the same as that of precipitation, with runoff from May to September comprising 45–54% of the total annual runoff. Zonal soils in the basin primarily consist of red soil and yellow soil [31].

The Xitiaoxi River serves as a crucial navigable waterway in the northern region of Zhejiang Province. The upper reaches of the river are characterized by extensive mining activities, with abundant stone resources lining its shores. However, since 2000, regulations necessitate ore washing during the manufacturing process, leading to substantial sediment discharge into the river and subsequently increasing river turbidity.

3. Framework Development

The proposed framework comprises several key steps outlined as follows: (a) preprocessing remote sensing images, including radiometric calibration, atmospheric correction, geometric correction, and extraction of the study area; (b) constructing and validating a model to invert turbidity in the Xitiaoxi River by integrating field-measured data and satellite data; (c) quantifying the turbidity in the Xitiaoxi River over the past 40 years and describing the spatial–temporal variation using the established inversion model; (d) analyzing the driving factors of turbidity in the study area by integrating socioeconomic data and correlation analysis (Figure 2). This framework aimed at developing a turbidity inversion model for the Xitiaoxi River using Landsat satellite data. This entails analyzing the temporal and spatial patterns of turbidity over the past 40 years, discussing the factors influencing turbidity in the Xitiaoxi River, and investigating the temporal and spatial fluctuations in turbidity and its specific drivers in similar high-turbidity navigable rivers.

3.1. Data Sources

3.1.1. In Situ Measurement Data

As shown in Figure 2, we set up 14 sampling sites that are evenly distributed along the Xitiaoxi River (

Table 1. Information on the sampling sites.

Sites	Location	Sites	Location
S1	Downstream of Xitiaoxi River	S8	Huxi River
S2	Gangkou station	S9	Dipuxi River
S3	Xiaoshugang station	S10	Downstream of Xixi River
S4	Middle reaches of Xitiaoxi River	S11	Longwangxi River
S5	Hunnigang station	S12	Middle reaches of Xixi River
S6	Hengtangcun station	S13	Nanxi River
S7	Upper stream of Xitiaoxi River	S14	Upper stream of Xixi River

). This study used data from five field surveys from September 2020 to July 2021. The testing equipment utilized was the YSI6600-V2 multi-parameter water quality detector manufactured by YSI Company, Yellow Springs, OH, USA. The measuring range was of 0–1000 NTU, resolution 0.1 NTU, accuracy reading ±2% or 0.3 NTU, whichever was greater. This compact yet versatile instrument is well-suited for multi-point continuous sampling across diverse water bodies. Measurements were taken by immersing the instrument in the water, with each point measured three times. Following the removal of outliers, water quality parameters at each point were obtained, with turbidity data reported in NTU (Nephelometer Turbidity) units.

According to the survey findings illustrated in Figure 1, turbidity ranged from 2 to 155 NTU. Obviously, Downstream of Xitiaoxi River (S1), Gangkou station (S2), and Xiaoshugang station (S3) exhibited relatively high turbidity, with mean values of 76, 83, and 72 NTU, respectively. The downstream region, particularly the Gangkou station, experienced extreme turbidity due to sediment accumulation in the complex plain river network area. The frequent passage of ships at the Gangkou station stirred up suspended solids and sediment, contributing to the highest turbidity. In contrast, other monitoring sites exhibited average turbidity ranging between 7 and 34 NTU. These survey results vividly illustrate the turbidity disparities between the upper and lower reaches of the Xitiaoxi River, which reflects the suspended sediment in the river from the side (Figure 3).

3.1.2. Satellite Data

Investigating the temporal and spatial distribution of turbidity in the Xitiaoxi River through one or two large-scale in situ surveys is insufficient and demands significant manpower and resources. High-resolution satellite remote sensing images can be used to invert the spatial and temporal distribution of turbidity in historical rivers. Satellites such as Landsat 5, launched in 1984, and Landsat 8, launched in 2013, have provided Earth image data for nearly 40 years. Their 16-day revisit period and 30 m spatial resolution offer long-term data suitable for monitoring water bodies.

Launched in March 1984, Landsat 5 is an optical Earth observation satellite equipped with a Thematic Mapper (TM) (NASA Landsat Science, Reston, VA, USA) and Multi-Spectral Scanner (MSS) (NASA Landsat Science) payload. The imagery captured by the Landsat 5 satellite remains the most widely used and effective remote sensing data source of Earth resource satellites globally. Additionally, Landsat 5 holds the distinction of being the longest-operating optical remote sensing satellite in orbit. On 11 February 2013, NASA successfully launched the Landsat 8 satellite. Landsat 8 is equipped with two sensors: the OLI (Operational Land Imager, NASA Landsat Science) and the TIRS (Thermal Infrared Sensor, NASA Landsat Science) (

Table 2. Spectral information on Landsat 5 and Landsat 8.

		Band	Spectrum Range (µm)	Spatial Resolution (m)
Landsat 5	Band 1	Blue	0.45–0.52	30
	Band 2	Green	0.52–0.60	30
	Band 3	Red	0.63–0.69	30
	Band 4	NIR	0.76–0.90	30
	Band 5	SWIR	1.55–1.75	30
	Band 6	LWIR	10.4–12.5	120
	Band 7	SWIR	2.08–2.35	30
Landsat 8	Band 1	Coastal	0.43–0.45	30
	Band 2	Blue	0.45–0.52	30
	Band 3	Green	0.53–0.60	30
	Band 4	Red	0.63–0.68	30
	Band 5	NIR	0.85–0.89	30
	Band 6	SWIR1	1.56–1.67	30
	Band 7	SWIR2	2.10–2.30	30
	Band 8	Pan	0.50–0.68	15
	Band 9	Cirrus	1.36–1.39	30
	Band 10	TIRS1	10.60–11.19	100
	Band 11	TIRS2	11.50–12.51	100

).

In this study, using Landsat 8 satellite OLI data to invert Xitiaoxi River turbidity, the satellite images need to be preprocessed first, including radiometric calibration, geometric precision correction, and atmospheric correction. Firstly, radiation calibration generally refers to the acquisition of remote sensing data, usually gray value (DN) into the actual physical quantity (such as radiation brightness or reflectivity, etc.). The FLAASH module in ENVI5.3 is used for atmospheric correction. FLAASH adopts the MODTRAN4 radiation transmission model, which can effectively reduce the atmospheric influence and obtain the true surface reflectivity [19]. If the image is distorted, geometric precision correction is needed, and the image is resampled by cubic convolution interpolation and the error is controlled within 0.5 pixels.

3.1.3. Socioeconomic Data

The socioeconomic data of Anji County were sourced from the statistical yearbooks of Huzhou City from 2001 to 2023. These datasets include information on resident population, GDP, the number of sailing vessels, and the total output value of the mining industry. Additionally, sediment discharge data for the Gangkou station were obtained from Huzhou water Resources Bureau covering the period from 2001 to 2022.

3.2. Turbidity Inversion Methods

3.2.1. Model Building

In various study areas, differences in particle size, density, phytoplankton concentration, or colored soluble organic matter necessitate the selection of the optimal band or band combination for empirical or semi-empirical models tailored to the region [32]. In this study, an empirical model was developed to estimate water turbidity. The approach involves identifying the statistical relationship between measured turbidity and spectral information from satellite imagery using statistical methods. Subsequently, an inversion equation is established to derive turbidity distributions on a long time series.

Following a correlation test of single bands and various band combinations, it was observed that the green band, red band, and near-infrared band are more sensitive and single bands 2 and 4, as well as the sum of bands 3 and 4 (Landsat 8), exhibit strong correlations with the measured turbidity. Based on these remote sensing factors, different index models were developed, yielding correlation coefficients of 0.901, 0.935, and 0.912, as depicted in Figure 4.

3.2.2. Model Validation

In this study, a total of 70 sample data were utilized, with 45 allocated for model construction and 25 for verification purposes. To validate the models, common statistical indicators were employed to analyze the errors associated with each inversion model. These indicators include the root mean squared error (RMSE), mean absolute percentage error (MAPE) [20], and Nash–Sutcliffe efficiency coefficient (NSE) [33]. The calculation formulas for each index are outlined as follows:

$$RMSE=\sqrt{{\displaystyle \frac{1}{N}}{\sum }_{i=1}^N{\left(V_i^P-V_i^M\right)}^2}$$

(1)

$$MAPE={\displaystyle \frac{1}{N}}{\sum }_{i=1}^N\left|{\displaystyle \frac{V_i^P-V_i^M}{V_i^M}}\right|\times 100\%$$

(2)

$$NSE=1-{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\left(V_i^P-V_i^M\right)}^2}{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\left(V_i^M-\overline{V^M}\right)}^2}}$$

(3)

where $V_i^P$, $V_i^M$, $\overline{V^M}$, and N represent forpredicted value, measured value, average value of measured value, and the number of measured values (i = 1, 2, 3, …, N), respectively.

To directly compare the difference between the inverted turbidity and the measured turbidity, our study generated scatter plots using the inversion results from the three models alongside the measured results. Each scatter plot includes a linear regression fitting line (red line), fitting equation, a 1:1 line (dash line), and evaluation indices for scatter points (Figure 5.). Based on the model verification results, the model established using band 4 exhibits the best performance and superior fitting (R² = 0.805, RMSE = 7.9 NTU, MAPE = 0.58, NSE = 0.75). Consequently, inversion models based on either band 3 (Landsat 5) or band 4 (Landsat 8) were determined, with the fitting equation given as y = 2.1216e^0.502x.

3.3. Driving Factors Analysis

This study employed ENVI 5.3 software to calculate the multi-year average values of the inversion images, enabling the analysis of turbidity’s spatial distribution in the Xitiaoxi River. Additionally, we explored the variation of turbidity over the past 40 years using time series analysis methods.

Correlation analysis involves assessing the degree of correlation between two or more variables to understand their relationship. A correlation heat map is a widely used visualization tool for analyzing the correlation between various environmental factors, particularly in multivariate analysis [34]. Before investigating the relationship between target factors and variables, it is essential to examine the correlation between variables. In this study, impact factors such as Normalized Differential Vegetation Index (NDVI) [35], sediment discharge, GDP, population, the number of sailing vessels, and the total output value of the mining industry were considered. The aim was to explore the correlation between these factors and monitoring stations, and to analyze the specific factors influencing river turbidity. The Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs):

$$NDVI={\displaystyle \frac{NIR-RED}{NIR+RED}}$$

(4)

where NIR represents the reflectance of the near-infrared band, and RED represents the reflectance of the red band.

4. Results

4.1. Spatial Distribution of Turbidity in Xitiaoxi River

4.1.1. Spatial Distribution of Average Annual Turbidity

According to the established model, the turbidity of the Xitiaoxi River from 1984 to 2022 was inverted, and a spatial distribution map of the mean turbidity over the past 40 years was generated (Figure 6). Analysis reveals that water bodies with turbidity less than 50 NTU constitute 36% of the total area, while those with turbidity ranging between 50 and 100 NTU account for 38.7%. The remaining 25.4% of water bodies exhibit turbidity levels exceeding 100 NTU. Overall, turbidity tends to increase from the upstream to the downstream sections of the river, with clearer water observed in the upstream compared to the downstream sections. Particularly, the lowest turbidity value of 11 NTU is recorded in the upper channel of the river, while the highest value of 298 NTU is observed downstream, near Gangkou station.

The upstream regions mainly consist of forested areas with significant variations in river width and water depth, coupled with minimal shipping activities, resulting in relatively clear water conditions. Conversely, the middle reaches represent a transitional zone from mountainous to plain terrain, with increased turbidity attributed to reduced vegetation coverage along the banks. In the downstream sections, land use is predominantly characterized by construction and farmland, leading to heightened pollution from agricultural non-point sources. Frequent sand dredging activities contribute to increased turbidity due to the stirring up of bottom sediments. Additionally, the turbidity decreased due to the inflow of the Dongtiaoxi River to dilute the suspended solids at the Xitiaoxi River estuary [36].

4.1.2. Spatial Distribution of Seasonal Turbidity

On a seasonal scale, the turbidity during spring, summer, autumn, and winter was also derived using the established model, and spatial distribution maps of seasonal mean turbidity were generated (Figure 7). The findings indicate consistently lower turbidity in the upstream compared to the downstream across all seasons. The average turbidity in the river during spring, summer, autumn, and winter was recorded as 93.9, 111.3, 113.5, and 120.9 NTU, respectively.

According to the statistical results (Figure 8), in spring, the turbidity of the river is the highest in the range of 50–100 NTU, accounting for 54.2%, followed by the water area with turbidity distribution in the range of 100–150 NTU, accounting for 23.2%. Turbidity below 50 NTU accounted for 13.7%, and turbidity above 150 NTU accounted for 9%. In summer, the distribution of turbidity across different ranges (0–50, 50–100, and 100–150 NTU) is relatively balanced, with proportions of 26.8%, 27.5%, and 24.4%, respectively. However, the proportion of water areas with turbidity exceeding 150 NTU increases significantly to 21.3%, twice as much as in spring. Similarly, in autumn, the distribution characteristics of turbidity in autumn mirror those in summer, with proportions of 27.7%, 24.3%, and 22.3% for the 0–50, 50–100, and 100–150 NTU ranges, respectively. Additionally, 25.7% of water areas exhibit turbidity exceeding 150 NTU. During winter, turbidity levels below 100 NTU account for 74.7% of the total, with 36% falling within the 0–50 NTU range and 38.7% within the 50–100 NTU range. Water areas with turbidity exceeding 150 NTU represent 15.8%, slightly higher than in spring.

It is concluded that the turbidity of the Xitiaoxi River is lowest in spring, indicating relatively clear water conditions, followed by summer and autumn, with the highest turbidity observed in winter. These seasonal variations are speculated to be associated with changes in natural factors such as vegetation cover, precipitation, and wind speed.

4.2. Temporal Variation Inof Annual Turbidity in Xitiaoxi River

Figure 9 illustrates the trend in annual mean turbidity and the long-term time series at 14 monitoring points, with the dotted line indicating the trend in interannual turbidity change. The turbidity in Dipuxi River, Nanxi River, and the upper stream of Xixi River remain below 100 NTU throughout the year. This is primarily attributed to the steep upstream slope of the Xitiaoxi River, strong river scour and high vegetation coverage, particularly consisting of mountain forests, which contributes to clearer water conditions upstream. Statistical analysis reveals that turbidity fluctuations generally show an initial increase followed by a decrease. Data from eight monitoring points suggested that 2005 marked an inflection point in turbidity change. Before 2005, turbidity exhibited a fluctuating upward trend, followed by a fluctuating downward trend after 2005. Moreover, turbidity at all monitoring points displayed a fluctuating downward trend after 2005, possibly attributed to sand mining activities and subsequent mine rehabilitation efforts. Among the monitoring points, those in the upper reaches are less affected by human-induced sand mining activities, and the suspended sediment, aided by the steeper upstream slope, is less prone to settling. Conversely, monitoring points in the middle and lower reaches, particularly those in the main stream, are significantly impacted by sand mining activities, resulting in a pronounced change trend. Tributaries of the Xitiaoxi River are comparatively less affected by these activities.

5. Discussion

5.1. Impacts of River Sediment on Turbidity

Studies have consistently shown a strong correlation between turbidity and suspended matter concentration across varying salinities [37]. Suspended matter serves as the primary contributor to water turbidity. Given that Gangkou station serves as the control section downstream of the Xitiaoxi River, offering insights into the overall basin conditions, we specifically selected sediment data from this station to elucidate the influence of sediment on turbidity. The variation trend in turbidity closely mirrors that of sediment discharge at Gangkou station, indicating a significant correlation between sediment content and turbidity (R² = 0.842) (Figure 10). Consequently, the high turbidity observed in and around Gangkou station can be attributed to the frequent stirring up of sediment by passing ships.

5.2. Impacts of Vegetation Coverage on Turbidity

Several scientific studies have highlighted the significant role of dam construction in reducing sediment flux in major rivers globally, particularly in the northern hemisphere [38]. However, findings by Cao et al. suggest a time constraint on the sediment-containment capacity of dams, with watershed greening emerging as a crucial factor contributing to the sustained decline in sediment flux in large rivers entering the sea in China in recent years [39]. Using Landsat satellite data, long-term changes in suspended sediment concentration and vegetation coverage in river basins across China since the 1980s have been analyzed. The results indicate a substantial increase in vegetation coverage in major river basins since the 1990s, which is negatively correlated with river sediment concentration to varying degrees. This underscores the significant contribution of land greening to reducing river sediment flux in China. While dams have indeed exerted a considerable impact on river sediment flux, this study suggests that the influence of basin land use change is more enduring and far-reaching. Over the past two decades, China has implemented ecological restoration projects such as afforestation and the conversion of farmland to forest and grassland. These initiatives have not only bolstered land vegetation cover but also led to a reduction in river sediment flux and an enhancement of ecological services.

Figure 10 illustrates a robust correlation between turbidity and sediment discharge. Based on these findings, we hypothesize a potential correlation between turbidity and vegetation coverage in the Xitiaoxi River. To explore this further, we calculated the average annual change in the Normalized Differential Vegetation Index (NDVI) in the Xitiaoxi River Basin from 1984 to 2022 (Figure 11). Over this period, vegetation coverage in the Xitiaoxi River Basin has shown a gradual increase. Despite experiencing fluctuations, with vegetation coverage in 2022 slightly lower than that in 1984, the overall trend indicates a slight upward trend. By 2022, the average vegetation coverage in the basin reached 0.31. This observation aligns with the declining turbidity trend observed in the Xitiaoxi River over the same period. Wan’s study [40] further supports these findings, suggesting that natural forests and grasslands play a beneficial role in stream water quality. Conversely, excessive nitrogen and phosphorus emissions from agricultural areas have contributed to increased water eutrophication. This disparity likely explains the lower turbidity levels observed in the upper reaches of the Xitiaoxi River compared to the lower reaches.

The correlation between turbidity data from 14 monitoring stations and the NDVI in the basin was investigated, as depicted in Figure 12. However, the results fell short of expectations, with only a handful of sites demonstrating the anticipated correlation. Despite this, researchers maintain their belief in the existence of a relationship between vegetation cover and river turbidity. It is plausible that vegetation cover may exert minimal influence on turbidity, or that its effects are not directly linked to changes in turbidity. Alternatively, these effects could manifest indirectly and permeate throughout the natural circulation system, establishing intricate and interconnected relationships.

5.3. Impacts of Socioeconomic Factors on Turbidity

Natural phenomena such as precipitation, wind, runoff, soil erosion, and human activities including resident population (POP), gross domestic product (GDP), land use changes, sand transport, and mining, are potential factors influencing water turbidity in the Xitiaoxi River. In our analysis, we investigated the impact of resident population and GDP on turbidity, but the correlation between these factors and turbidity was not evident (Figure 12). Due to data limitations regarding sand extraction quantities, we explored the influence of the number of sailing vessels (NOSVs) and the total output value of the mining industry (TOVMI) on turbidity in the main stream of the Xitiaoxi River. The results reveal a strong correlation between these factors and turbidity. Socioeconomic data indicated an increase in the number of sailing vessels before 2005, followed by a decline after 2005, aligning with the trend observed at most monitoring points in the Xitiaoxi River. This suggests that the frequency of ship traffic in the river may impact turbidity fluctuations (Figure 13). The closure of the Yucun mine significantly affected turbidity in the Xitiaoxi River, followed by the gradual closure of 78 mines in Anji County and the implementation of a comprehensive mining ban policy in 2010. The total output value of the mining industry in Anji County is not solely linked to mining volumes but also influenced by market conditions in the mineral industry. Consequently, the total output value of the mining industry continued to rise between 2005 and 2010 but began to decline after 2010.

Since 2002, the Yucun village of Anji County (Figure 1) has gradually shut down the mine, and completely ceased production at the end of 2004. Presently, two of the coldwater cave mines have been restored and reclaimed, while one has been sealed and transformed into a mine site park. Before the comprehensive regulation of sand mining in the Xitiaoxi River, sand and gravel separators were mining sand in the river, and the river water was turbid. As a result, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005. After 2005, the water quality of the Xitiaoxi River improved, and the turbidity began to decline. This trend is particularly evident in the monitoring points on the main stream of the Xitiaoxi River, such as S1, S2, S4, S6, S7, and S8. This improvement can be attributed to a reduction in ship traffic and sand mining activities since 2005, resulting in lower turbidity due to decreased agitation of sediment by ships. The relevant survey data show that the number of sailing vessels has a strong correlation with turbidity. Since the 1970s, there have been 68 sand mining vessels in the creek, and since 2010, Anji County has issued a comprehensive ban on mining. Additionally, the implementation of projects like the “clean water into the lake” initiative, including the regulation of the Xitiaoxi River’s main stream, sedimentation control, ecological restoration efforts, and the Hunnigang station regulation, have contributed to maintaining water quality in the Xitiaoxi River above Class II throughout the year. Consequently, these human activities may have significantly impacted several monitoring sites in the main, middle, and lower reaches of the Xitiaoxi River. Moreover, data from all monitoring points indicate that, since 2014, the average turbidity across the entire river has remained below 40 NTU, indicating relatively clear water throughout the river system.

Local policies have also exerted significant influence on the turbidity of the Xitiaoxi River. Analysis of sediment content data from Gangkou station over the years reveals that, during the 1980s and 1990s, sediment content remained mostly below the annual average. However, after 2000, local regulations mandated the washing of stones during manufacturing, leading to the discharge of sand-containing wastewater into the river, resulting in an upward trend in sediment content and turbidity in the Xitiaoxi River. Particularly noteworthy is the sediment content in 2005, nearly twice that of previous years, a trend confirmed by inversion results. It was not until 2013, when the environmental protection department proposed comprehensive treatment measures for the water environment of the Xitiaoxi River, addressing issues related to sand mining and shipping, that an overall reduction in river turbidity began to be observed [36].

5.4. Reliability, Advantages, and Limitations of Current Study

There are few studies on navigable turbidity rivers in semi-hilly and semi-plain areas similar to the study area in this study, and the factors affecting turbidity in different study areas are different. However, the existing studies can only be used to show that the conclusions of this study are also reliable. Previous studies [1,20] have shown that the mean turbidity of different regions in winter is significantly higher than that in summer, which is consistent with the results of this study.

This study presented a framework for investigating the temporal and spatial fluctuations in turbidity and specific drivers in high-turbidity navigable rivers. Based on this framework, the temporal and spatial changes in rivers in such areas and the reasons for the changes are clear, which makes water ecological and timely management possible in similar areas. However, there were still some limitations which were as follows: firstly, the turbidity was mainly quantified by the statistical model in this framework, rather than a mechanism model. Secondly, for the analysis of the causes of turbidity changes in time and space, if there were public data, such as the annual mining volume or the daily volume of ships in the river, we could more clearly understand the causes of river turbidity variations.

6. Conclusions

In this study, temporal and spatial variation in turbidity were quantified and the driving factors were discussed by a new framework integrating in situ measurement data, satellite data, socioeconomic data, and a power index function model and correlation analysis which is suitable for the navigable and turbid river. The results are as follows:

(1)

By comparing different wavebands and their combinations, the inversion model is eventually established with the red wave band (y = 2.1216e^30.502x, R² = 0.912). The validation of the model results shows good performance and applicability.

(2)

The turbidity in the upstream region is generally lower than that in the downstream region. The lowest value appears in the upper channel of the river (11 NTU), and the highest value appears in the downstream region near Gangkou station (298 NTU). The average turbidity in the river in spring, summer, autumn, and winter was of 93.9, 111.3, 113.5, and 120.9 NTU, respectively.

(3)

Turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005. After 2005, the turbidity began to decline. This trend is very obvious at the monitoring points on the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and the Huxi River (S8).

(4)

Shipping and mining are the main reasons affecting the turbidity in the Xitiaoxi River. At the same time, the influence of local policies on turbidity in the Xitiaoxi River is also significant.