Next Article in Journal
Utilizing Recycled PET and Mining Waste to Produce Non-Traditional Bricks for Sustainable Construction
Next Article in Special Issue
Carbon-Oriented Eco-Efficiency of Cultivated Land Utilization Under Different Ownership Structures: Evidence from Arid Oases in Northwest China
Previous Article in Journal
Employment in Portugal’s Tourism Sector: Structural Transformation and Working Conditions from 2012 to 2022
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 19
10.3390/su17198838
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Agricultural Production Patterns on Surface Water Quality in Central China’s Irrigation Districts: A Case Study of the Four Lakes Basin

by
Yanping Hu
1,2,*,
Zhenhua Wang
1,2,*,
Dongguo Shao
3,
Rui Li
1,2,
Wei Zhang
1,2,
Meng Long
1,2,
Kezheng Song
1,2 and
Xiaohuan Cao
1,2
1
Changjiang River Scientific Research Institute, Wuhan 430010, China
2
Key Laboratory of Basin Water Resource and Eco-Environmental Science in Hubei Province, Wuhan 430010, China
3
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(19), 8838; https://doi.org/10.3390/su17198838
Submission received: 16 August 2025 / Revised: 28 September 2025 / Accepted: 30 September 2025 / Published: 2 October 2025
(This article belongs to the Special Issue Agricultural Environment Monitoring for Sustainable Agricultural Development)
Browse Figures
Review Reports Versions Notes

Abstract

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity.

1. Introduction

Agricultural non-point source (ANPS) pollution is a key driver of global freshwater ecological degradation, and improving surface water quality via regulating agricultural production models remains a common challenge in international environmental science and agricultural sustainability [1,2,3]. The United Nations Environment Programme (UNEP, 2019) reports that approximately 40% of global rivers and lakes face eutrophication risks due to agricultural nitrogen (N) and phosphorus (P) inputs, with more severe issues in concentrated irrigated agricultural areas. Four Lakes Main Canal (FLMC) is situated in the middle and lower reaches of the Four Lakes Basin (FLB) in the Jianghan Plain. Together with Changhu Lake and Honghu Lake, it forms a hierarchical drainage system covering the upper, middle, and lower reaches of the basin, thereby establishing an efficient farmland water conservancy system with both flood drainage and irrigation functions. For the basin’s agricultural production, this system provides core infrastructure by securing stable water supply and ensuring crop growth. It also serves as a key link to coordinate the “agricultural production-water system cycle”, while laying a crucial water system foundation for regional sustainable agriculture [4]. Since 2010, the water quality of the FLMC has been continuously deteriorating, which not only threatens the ecology of the Honghu Lake wetland but also affects agricultural irrigation along the coast, restricts the advancement of sustainable agriculture. Against this backdrop, exploring how agricultural planting models affect river/lake water quality in typical irrigation districts can both inform regional ecological governance and serve as a reference for managing ANPS pollution in similar regions globally [5,6].
Existing studies have shown that the water quality changes of both the FLMC and Honghu Lake are closely related to ANPS pollution in the basin [7,8,9]. The key influencing factors of ANPS pollution in the basin include land use types and spatial patterns [10,11,12], among which livestock and poultry breeding, aquaculture, and farmland cultivation are the main sources of ANPS pollution [6,13]. It is particularly noteworthy that N/P pollutants from freshwater aquaculture in rivers and lakes as well as livestock and poultry breeding along the coast directly enter the water body, leading to a sharp increase in N/P concentrations in the water and severely damaging the aquatic ecological environment [14,15,16]. Research results indicate that due to the extensive use of feed and chemical fertilizers in freshwater aquaculture at Changhu Lake, the contents of total phosphorus (TP) and total nitrogen (TN) in the water flowing from Changhu Lake into the FLMC exceed the standards by several to dozens of times. This has caused severe pollution in the upper reaches of the FLMC adjacent to Changhu Lake, significantly weakening the supply capacity of irrigation water for the lower reaches and posing a hidden threat to the water quality security of Honghu Lake [17,18]. To implement the Yangtze River Ecological Protection Strategy and promote the sustainable and coordinated development of agriculture and the water system in the basin, since the 12th Five-Year Plan period (2011–2015), municipal (prefectural)-level governments in Hubei Province have organized county-level governments to carry out rectification work on illegal aquaculture in rivers and lakes, and gradually banned cage freshwater aquaculture. Specifically, after two large-scale centralized demolition campaigns in 2015–2017 and 2019–2021, the cage freshwater aquaculture facilities in Honghu Lake were basically eliminated, effectively reducing the total amount of pollutants directly discharged into the water system [19]. Meanwhile, to simultaneously achieve water system protection and agricultural income increase, the local area had promoted integrated farming models dominated by rice-crayfish. The rice-crayfish model can theoretically balance agricultural production and water system protection, which is an important exploration direction for sustainable agriculture. In practice, driven by the rapid development of the crayfish industry, this model has demonstrated remarkable economic benefits, enhancing farmers’ enthusiasm for participation. However, some farmers have resorted to overfeeding in pursuit of short-term high profits. Compared with traditional single-cropping rice cultivation, this practice uses much more energy. It also brings about multiple water system issues: excess feed residue accumulates N/P in paddy soil, increasing soil leaching pollution risk; unused feed and crayfish excreta enter water, lowering dissolved oxygen, boosting COD, worsening N/P discharge into the water system, and elevating water eutrophication risk [20,21,22,23,24]. Nevertheless, the annual loss of N/P from rice-crayfish is still lower than that from freshwater aquaculture [21,22,25]. Therefore, developing rice-crayfish within a certain scope and banning cage freshwater aquaculture can theoretically reduce ANPS pollution, lower the environmental risks of rivers and lakes, and generally conform to the dual goals of “economic benefits and environmental friendliness” in sustainable agriculture [26,27]. Despite this, against the backdrop of large-scale adjustments to the agricultural farming models (AFMs), the characteristics of water quality changes in the FLMC and Honghu Lake as well as their response mechanisms remain unclear.
To address this critical scientific question, this study focused on the FLB, investigating how changes in AFMs affected water quality in the FLMC and Honghu Lake. Specifically, we identified interannual AFMs evolution in the basin using remote sensing and statistical yearbook data, analyzed spatiotemporal patterns of river/lake water quantity and quality with 2010–2023 hydrological/water quality monitoring data, and quantified the proportions and dynamic trends of N/P-loads from different sources over the past decade. Building on these, we revealed the response of river/lake water quantity/quality to AFMs changes via correlation heatmaps, ultimately providing scientific support for optimizing the basin’s AFMs management and developing sustainable agricultural models.

2. Materials and Methods

2.1. Study Area

The FLB is situated in the southern part of Hubei Province, China, covering three cities, namely Jingmen, Jingzhou, and Qianjiang. The total area of the basin is 11,547 km2, with an inner embankment area of 10,375 km2. The topographic feature of the basin is characterized by higher elevation in the northwest and lower elevation in the southeast. Specifically, the upper reaches of the basin are dominated by hilly areas, while the middle and lower reaches consist of a plain-lake region (Figure 1). Under natural conditions, the FLB is a water-rich region, with the Neijing River as its main water system. This basin receives water inflow from the hilly areas in the upstream and collects waterlogged water from the lake regions in the downstream. Historically, the basin has been frequently affected by water-related disasters, among which floods are the most prominent. Moreover, floods, waterlogging, and droughts often occur alternately in this region. To tackle these problems, a unified planning initiative was launched in 1955. This initiative involved a series of engineering measures, including the construction of embankments, the excavation of canals, the building of sluices, and the establishment of pumping stations. Over time, a comprehensive plain canal network was formed. This network is featured by three key characteristics: the separation of rivers and lakes, the division of drainage and irrigation systems, and the interconnection between rivers, lakes, and canals.
The FLMC within the basin originally belonged to a section of the Neijing River. It originates in the northwest of Jingmen City, Hubei Province, and flows through Shashi, Qianjiang, Jiangling, Jianli. Subsequently, it empties into the Yangtze River at Xintankou in Honghu City, with a total length of 358 km. The FLB is demarcated into upper, middle, and lower regions based on elevation. The FLMC is situated in the middle and lower regions of the FLB. Water from the upper region of the FLB first flows into Changhu Lake. It then either enters the FLMC through sluices (e.g., Leijiadang Sluice, Xijiakou Sluice, Liuling Sluice) or is discharged via the Tianguan Sluice. Water from the middle and lower regions of the FLB flows along the FLMC, passing through the East Main Canal, West Main Canal, and other branch canals. It then merges with the inflow from the upper region before entering Honghu Lake. Subsequently, the combined water is discharged from the FLMC through specific sluices and pumping stations, including the Xintankou Sluice, Xindi Sluice, and Gaotankou Pumping Station. Accordingly, to estimate the annual drainage volume of the FLMC, this study assumed the annual discharge volumes from the aforementioned sluices and pumping stations.

2.2. Data Sources and Monitoring Point Layout

The data used in this study primarily include TN/TP concentrations in the water of the FLMC, flow rates at typical cross-sections of the FLMC, rainfall data in the FLB, land use types in the FLB, and data on the main agricultural farming areas. Among these, the TN and TP concentration data for the national control sections (Yunlianghu C, Xinhecun E, and Xintankou K) of the FLMC from 2010 to 2023 were obtained from the China National Environmental Monitoring Centre (https://www.cnemc.cn/, accessed on 10 May 2024). Data on N/P concentrations at other monitoring sections were sourced from the monitoring data of the Jingzhou City Resident Tracking Project and monthly monitoring data from the funded project over the past three years. Flow rate data for the FLB were provided by the Jingzhou Four Lakes Engineering Administration Bureau. Rainfall data for the FLB were obtained from 1 km monthly precipitation dataset for China (1901–2024) (https://data.tpdc.ac.cn/login, accessed on 20 March 2025). The 1 km grid land use data for the FLB were sourced from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/, accessed on 20 March 2025). Data on the main agricultural farming areas in the FLB were derived from the annual statistical yearbooks published by the Hubei Provincial Bureau of Statistics (https://tjj.hubei.gov.cn/, accessed on 21 March 2025).
Taking 2022 as an example, the monthly data on TN and TP concentrations in the FLMC were obtained from regular monthly monitoring by the research team. Monitoring points were primarily located in residential areas, upstream and downstream of main and branch canal confluences, and at the inlets and outlets of lakes and reservoirs. These include the upper region of the FLB (A: Changhu Lake), the middle region (B–H), the lower region (I–K), and the Yangtze River estuary (L) (Figure 1). For ease of analysis, the monitoring points were divided into the canal head (A–B), the middle canal (C–J), and the canal tail (K–L).

2.3. Research Methods

(1)
Determination of Water Quality
Methods for measuring water quality indicators referred to Water and Wastewater Monitoring and Analysis Methods (Fourth Edition) [28]. The alkaline potassium persulfate digestion-UV spectrophotometric method was employed to determine the concentration of TN, and the ammonium molybdate spectrophotometric method was employed to determine the concentration of TP.
(2)
Estimation of Water Yield and Different Pollution Loads in the FLB
Land use type data, major agricultural farming area data, and total industrial production discharge data in the FLB (2010~2023) were retrieved from the annual statistical yearbooks issued by the Hubei Provincial Bureau of Statistics. The water yield for different land use types was estimated via the Soil Conservation Service (SCS) model [29]. The Export Coefficient Models were employed to estimate the ANPS loads in the FLB. Meanwhile, the industrial production pollution loads were calculated using pollutant discharge coefficients, which were derived from the Manual of Pollution Generation and Emission Coefficients for Industrial Pollution Sources. The export coefficients of non-point source (NPS) pollution were determined based on the N/P export coefficients of different AFMs identified in this study, which were observed in Shashi District, Qianjiang City, Honghu City, Jiangling County, etc., and with reference to the average values of relevant studies in the Jianghan Plain, where the social activities and natural conditions are similar to the FLB [30,31]. The export coefficient values for different AFMs are shown in Table 1.
The excretion coefficients for livestock breeding and the population export coefficients for rural domestic pollution were derived from the recommendations of the National Environmental Protection Agency [32]. N/P-loads in the FLMC were derived from its annual average water volume, N/P concentrations. Meanwhile, the annual average inflow volume into Honghu Lake, along with the N/P concentrations of this inflow, were used to compute the N/P-loads into Honghu lake.
Table 1. The pollution output coefficient of different AEMs [kg/(hm2·a)].
Table 1. The pollution output coefficient of different AEMs [kg/(hm2·a)].
Study AreaPollutantsAFMsSource
Rice Cultivation (RC)Rice-Crayfish Farming (RCF)Freshwater Aquaculture (FA)Dryland Cropping (DC)
Qianjiang CityTN-9.7--[25]
TP-2.2--
Qianjiang CityTN11.844.46--[33]
TP0.510.68--
Honghu CityTN--27.61-[34]
TP--7.33-
Three Gorges Reservoir AreaTN---17.1[35]
TP---2.3
Xiantao CityTN21.60--21.22[36]
TP2.50--2.05
This ResearchTN16.577.1028.0518.75Observation
Data
TP1.492.557.412.09
MeanTN16.677.0927.8319.02
TP1.51.817.372.15

2.4. Statistical Analysis

The inter-annual N/P concentration data for the FLMC from 2010 to 2023 are presented as the mean ± standard error of all sampling points and sampling times for each year. The intra-annual variation data of TN/TP concentrations in the FLMC for 2022 are presented as the mean ± standard error of all sampling points and sampling times for each month of that year. The spatial variation data of TN/TP concentrations in the FLMC for 2022 are presented as the mean ± standard error of all sampling times for each monitoring point (A–L) during that year. The N/P NPS -loads for different AFMs were estimated based on the Export Coefficient Models, and the basin water yield was estimated using the SCS model. Stacked area charts and correlation heatmaps were generated. All data in this study were organized and analyzed using Excel and SPSS 19.0 software. Scientific data graphs were plotted using Origin 2021 software, and the sampling point distribution map was created using ArcGIS 10.2 and CAD 2014 software.

3. Results

3.1. Dynamics of Land Use Types and AFMs

Human-dominated land use types, such as construction land and cultivated land, significantly exacerbate water quality deterioration due to high-intensity human activities. Meanwhile, land use types with ecological regulation functions, including forestland and grassland, can contribute to water quality improvement through water conservation and pollutant purification, which is of great significance for maintaining the ecological sustainability of the basin [37,38]. Using remote sensing interpretation and statistical yearbook data of FLB land use, this study analyzed spatial distribution, conversion patterns of land use and AFM variations.
Figure 2 showed FLB’s land use structure was dominated by cultivated land (about 67%), water bodies (about 15%), and construction land (about 13%). Notably, cultivated/construction land concentrated in the basin’s central region, while water bodies distributed in the lower reaches [13]. Additionally, accelerated urbanization has driven continuous conversion of cultivated land to construction land in Jingzhou, Qianjiang, Jianli, Honghu, and Jiangling urban areas, which may promote urban development but encroach on ecological space. Conversely, lower-reach natural water bodies (e.g., Honghu Lake) had been converted to cultivated land via reclamation, which may reduce the basin’s ecological capacity and threat ecosystem sustainability (Figure 3a). Overall, 2010~2023 saw continuous expansion of FLB’s cultivated/construction land and annual decline of ecologically valuable water bodies (Figure 3b). Notably, this trend revealed that the unregulated expansion of cultivated and construction land could further worsen the water quality of the basin’s rivers and lakes, impeding the integrated sustainable development of the FLB’s social, economic, and ecological sectors [39,40].
Cultivated land is the primary land use type in the FLB, mainly used for DC, RC, integrated rice-aquaculture (e.g., rice-crayfish, rice-fish, rice-crab), and FA. Based on statistical yearbook data from Jingmen City, Jingzhou City, and Qianjiang City, the area changes of different AFM in the FLB from 2010 to 2023 were compared and analyzed (Figure 3c). Results demonstrated that the area of distinct AFM in the FLB exhibited substantial interannual changes, particularly marked since 2016. With the rapid development of rice-crayfish farming and the ban on net enclosure aquaculture, the proportion of rice-crayfish farming area increased by 14%, while the proportion of dryland crop area decreased by 11%, and freshwater aquaculture area decreased by 4% [19]. Given the differences in pollution generation and discharge among various AFM, shifts in AFM are certain to influence both NPS N/P -loads and the water quality of rivers and lakes within the FLB [41].

3.2. Dynamics of N/P-Loads Within Basin

Based on statistical yearbook data and on-site observations, the export coefficient method was used to estimate the N/P-loads (Figure 4). Compared with Period 1 (2010–2016), the N/P-loads in the FLB decreased significantly during Period 2, with average values declining from 39,688 t/a and 11,307 t/a to 35,671 t/a and 10,271 t/a, respectively. The variation patterns of N/P-loads entering the canal and lake were consistent with those of basin-wide N/P-loads, and the average values of all these loads decreased significantly. This indicated that optimizing AFMs and reducing NPS N/P-loads were of great significance for improving the water quality of rivers and lakes.
Based on statistical yearbook data from Jingmen City, Jingzhou City, and Qianjiang City from 2010 to 2023, the export coefficient method was used to estimate N/P-loads from seven different sources in the basin: dryland cropping (DC), rice cultivation (RC), rice-crayfish farming (RCF), freshwater aquaculture (FA), livestock and poultry breeding (LPB), rural living (RL), and industrial production (IP). The N/P-loads from different sources were statistically calculated (Figure 5). Results indicated that RC, RCF, FA and LPB were the dominant sources of N/P in the FLB, together accounting for roughly 80% of the N/P-loads within basin. Among these, the changes in N-loads are ranked as RCF > RC > FA > LPB, while the changes in P-loads are ranked as RCF > FA > LPB > RC. The results indicated that ANPS were the primary factor, which influenced the N/P-loads and water quality changes in FLMC, among which RCF and FA were the key factors. This is consistent with findings from most related studies [7,8,42].

3.3. Inter-Annual Variations in Water Quantity and Quality

Changes in AFM can affect regional water cycles and water quality [43,44]. Using 2016 as a time node, this study divided the period 1 into the pre-large-scale adjustment phase of AFM and the period 2 into the large-scale adjustment phase. The inter-annual variations in water quantity and quality of the FLMC during these two periods were analyzed. As shown in Figure 6a, the multi-year average drainage volume of the FLMC increased from approximately 6.0 billion m3 (period 1: 2010–2016) to 7.2 billion m3 (period 2: 2017–2023). The multi-year average TN concentration in the FLMC decreased from about 5 mg/L to 3 mg/L, and the multi-year average TP concentration decreased from approximately 0.21 mg/L to 0.17 mg/L. According to investigations, to meet the agricultural irrigation demands in the middle and lower regions of the FLB, the Yangtze River-Han River Water Diversion Project had supplied 5–6 billion m3 of water to Changhu Lake and the Dongjing River since it was put into operation in 2014. This was one of the key reasons for the increase in the multi-year average drainage volume of the FLMC from 2012 to 2023. Additionally, rainfall intensity and land use practices also influence the drainage volume of the FLMC [42,45]. The TN/TP concentrations declined in the FLMC, which was likely attributed to reduced ANPS pollution and the dilution effect from water supplementation to Changhu Lake [44,46,47].
Honghu Lake primarily receives water from the FLMC, so the water quantity and quality of the main canal affect the water quality of Honghu Lake [42]. Based on the annual drainage volume of the Xindi Sluice, the annual average water exchange volume of Honghu Lake was estimated. The results in Figure 6b showed that from period 1 to period 2, the multi-year average water exchange volume of Honghu Lake increased from approximately 1.93 billion m3 to about 2.41 billion m3 [48]; The multi-year average TN concentration in Honghu Lake increased from about 0.93 mg/L to 1.39 mg/L, and the multi-year average TP concentration increased from approximately 0.04 mg/L to 0.10 mg/L. During period 2, the water exchange volume of Honghu Lake increased by about 1.25 times compared to the period 1, which may be closely related to changes in the water quantity of the FLMC [49,50]. Nevertheless, while TN/TP concentrations in the main canal decreased, those in Honghu Lake did not exhibit a downward trend.

3.4. Intra-Annual Variations in Water Quantity and Quality

The RCF and FA are mainly concentrated in the middle region of the FLB. Water from various main and branch canals in the middle region converges into the FLMC and then enters Honghu Lake through the Futiansi Sluice. Taking the Futiansi Sluice and Honghu Lake as the research objects, this study analyzed the intra-annual variations in water quantity and quality of the FLMC during the flood season (April–September) and non-flood season (October–March of the following year). As shown in Figure 7a, the drainage volume and TN/TP concentrations in the FLMC exhibit significant seasonal variations. Both the water discharge and TP/TN concentrations in the main canal during the flood season were higher than those during the non-flood season. This was attributed to the overlap between the agricultural production period (April–September) and the flood season in the basin. A large amount of agricultural wastewater generated from RCF and FA was discharged centrally through sluices and pumping stations along the FLMC, which increased the water volume and TN/TP concentrations in the canal [51]. Calculations indicated that the cumulative water discharge during the flood season accounts for 65.7% of the annual total, while the cumulative emissions of nitrogen and phosphorus account for 60.4% and 67.2% of the annual totals, respectively. This suggested that ANPS pollution during the flood season may be the primary factor driving changes in TN/TP concentrations in the FLMC. The southeastern part of Honghu Lake was connected to the Yangtze River via sluices, while its northern part was separated from the FLMC by embankments. However, there were inlets to Honghu Lake at locations such as the Lantian River Estuary and Xiaxin River Estuary of the FLMC, allowing water from the FLMC to flow into the lake [48]. The results showed that the water discharge of Honghu Lake during the flood season is higher than that during the non-flood season, but the variations in its internal TN/TP concentrations were not significant (Figure 7b). The increased input of external TN/TP into Honghu Lake during the flood season had little impact on its TN/TP concentrations, which may be related to factors such as the high water level of Honghu Lake and the absorption of TN/TP by aquatic plants [48].

4. Discussion

4.1. Analysis of Driving Factors for N/P Concentrations Changes in the FLMC

The spatiotemporal variations in TN/TP concentrations in the FLMC were not driven by a single factor, but rather the synergistic effects of adjustments to AFMs, fluctuations in hydrological conditions, and spatial differences in pollutant inputs.
(1)
From the perspective of AFMs, RCF, RC, FA and LPB were the four AFMs with the largest proportions and changes in agricultural N/P-loads (Figure 5). Correlation analysis results (Figure 8) show that the N/P-load from AF was negatively correlated with that from RCF, while it was significantly positively correlated with the N/P-loads from FA and LPB (p ≤ 0.001, correlation coefficient > 0.89). Meanwhile, the TN/TP concentrations in the FLMC were also significantly positively correlated with the N/P-loads from AF (p ≤ 0.001, correlation coefficient > 0.84). It can be seen that AFM adjustments in the FLB had promoted a reduction in basin-wide N/P-loads and a decrease in FLMC N/P concentrations to a certain extent. Specifically, from 2017 to 2023, the proportion of FA area decreased by 4%, the proportion of DC area decreased by 11%, and the proportion of RCF area increased by 14% (Figure 3c). Correspondingly, the total N-loads in the FLB decreased by 2432 tons and the total P-loads decreased by 540 tons (Table 2), ultimately driving a continuous decline in N/P concentrations in the FLMC (Figure 9) [19,21]. Notably, although the TN/TP concentrations in the FLMC are negatively correlated with RCF, this does not justify the unlimited expansion of RCF. As of 2023, RCF had become the primary pollution source, contributing approximately 42% of the total N/P-loads (Table 2). Its potential impacts on the regional water environment cannot be overlooked, and the associated pollution risks require further in-depth research.
(2)
From the perspective of hydrological conditions, rainfall (P), main canal discharge (S), and branch canal discharge (R) affect N/P concentrations by altering water dynamic conditions. On the one hand, the high runoff caused by the annual peak rainfall (June–July) during the flood season (April–September) can reduce the N/P concentrations in the main canal through a dilution effect [51]. On the other hand, the ecological water supplement from the Yangtze River-Han River Water Diversion Project has increased the multi-year average drainage volume of the main canal from 6.0 billion m3 to 7.2 billion m3, further amplifying the dilution effect [44,46].
However, it is important to note the “double-edged sword” effect of hydrological conditions: during the flood season, RCF requires frequent water exchange (once per day on average during the feeding period from April to June), which flushes the N/P accumulated in the farming areas (the TN/TP concentrations in the water reach a peak one day before feeding) into the main canal. This results in a slightly higher TP concentration during the flood season (0.16 mg/L) than in the non-flood season (0.15 mg/L), creating a complex situation where “dilution and pollution input coexist” [52,53].
(3)
From the perspective of spatial differences in pollutant inputs, the N/P concentrations in the main canal exhibit a “first increase, then decrease” trend along the “canal head-middle canal-canal tail” gradient (Figure 10), which essentially reflected the uneven spatial distribution of agricultural pollution in the basin.
The water at the canal head (Section A–B) originated from Changhu Lake. Since the implementation of the ban on river-lake enclosure aquaculture, Changhu Lake has mainly received inflow from the hilly upper reaches of the FLB and supplementary water from the Yangtze River, resulting in relatively good water quality. The water in the middle canal (Section C–J) was primarily sourced from the main and branch canals as well as irrigation sluices in the central FLB. Dominated by integrated rice-aquaculture and freshwater aquaculture, this region has large drainage volumes and relatively poor water quality. The water at the canal tail (Section K–L) mainly came from the lower reaches of the FLB; due to the smaller catchment area here, the pollutant load was lower than that in the central region, leading to a slight improvement in water quality at the canal tail compared to the middle canal [54,55]. This spatial pattern confirmed that the agricultural-intensive central basin was the core source of N/P pollution in the main canal, and also a key area for pollution control.

4.2. Why Did Honghu Lake’s Water Quality Deteriorate During 2017–2023?

A paradox emerged during 2017–2023: while N/P-loads at the FLB scale decreased, water quality in the FLMC improved, and N/P inputs from the FLMC to Honghu Lake diminished, Honghu Lake’s water quality continued to deteriorate (Figure 9). This phenomenon highlights the complexity of aquatic ecosystem responses to agricultural and environmental interventions.
(1)
External Inputs: A Reduction in Quantity Does Not Equal Qualitative Safety
Although the optimization of AFMs reduced the N/P-loads in the FLB by 11–13%, and decreased the N/P inputs from the FLMC to Honghu Lake by 22.04% and 2.02%, respectively, the residual external pressure was still sufficient to hinder the improvement of Honghu Lake’s water quality. This is mainly due to the following reasons:
①
The background concentrations of N/P in the FLMC remain relatively high. Despite a slight decrease in the FLMC’s N/P concentrations (TN: 5 mg/L to 3 mg/L; TP: 0.21 mg/L to 0.17 mg/L), these concentrations were still much higher than those of Honghu Lake before disturbance (Period 1: TN 0.93 mg/L, TP 0.04 mg/L) (Figure 6). When such relatively polluted water enters Honghu Lake, it still raises the lake’s nutrient background value. Moreover, as a semi-enclosed lake, Honghu Lake has slow water exchange, making it difficult to achieve rapid purification [8,16]. For instance, the TN concentration in the FLMC in 2023 was still 1.5 times that of Honghu Lake in 2017 (Figure 9), which means that even with a reduction in inflow volume, the FLMC still acts as a “nutrient source” rather than a “purification channel” for Honghu Lake.
②
Inadequate control of NSP in the central basin. The central FLB, dominated by RCF and FA, remains the main source of N/P-loads [42]. Agricultural wastewater generated in this region was discharged into Honghu Lake through the FLMC and its tributaries, continuously supplementing the lake’s external nutrient pool and offsetting the improvement effects brought by load reductions from other pollution sources. This was well evidenced by the higher N/P concentrations in the middle section of the FLMC and the northern waters of Honghu Lake (Figure 10).
③
Short-term pollution pulses from the demolition of net enclosures/polders further exacerbate external pressure. Although the two-phase demolition projects (2015–2017, 2019–2021) eliminated direct aquaculture pollution on the lake surface, the high-concentration wastewater (TN ≈ 15 mg/L, TP ≈ 1.2 mg/L) remaining in the abandoned net enclosures was discharged into the lake in a concentrated manner in the short term, causing a 15–20% short-term increase in the lake’s TN/TP concentrations [56,57]. Meanwhile, the disturbance caused by construction machinery also triggers the release of nutrients from surrounding sediments, forming a “legacy pollution effect” that lasts for 3–5 years and further increases the external pollution load [58].
(2)
Internal Release: Increment of Hidden Pollution
As a shallow lake (with an average water depth of approximately 1.5 m), the historical nutrients accumulated in Honghu Lake’s sediments serve as the “stock base” for internal release. Environmental disturbances, however, convert this “stock” into “increment,” making internal release the dominant factor driving water quality deterioration. In the 2023 investigation on Honghu Lake’s N/P-loads conducted by the research team, it was found that the annual average internal phosphorus release in Honghu Lake was approximately 220 tons, accounting for 23.2% of the total inflow load and 80% of the inflow reduction during 2017–2023.
Furthermore, extreme climate events such as the 2016 flood and 2022 drought severely damaged Honghu Lake’s ecosystem, leading to the decline of submerged vegetation (with sluggish recovery) and reduced self-purification capacity. Meanwhile, the decay of vegetation intensified the internal release of N/P. These changes collectively formed a vicious cycle of “water quality deterioration → vegetation decline → further water quality deterioration,” continuously amplifying the internal pollution effect [58,59].
(3)
Other Potential Factors: Unquantified Variables
In addition to the aforementioned core factors, climate change and atmospheric deposition may also be potential drivers of the increasing N/P concentrations in Honghu Lake. Climate change may exacerbate the migration of agricultural NPS pollution by altering the temporal and spatial distribution of rainfall (e.g., increased frequency of heavy rains during the flood season). Meanwhile, the increases in water volume, N/P discharge, and water turbidity during the flood season may further threaten the stability of Honghu Lake’s aquatic ecosystem (Figure 7) [60]. Atmospheric deposition, on the other hand, may input N/P into Honghu Lake through dry and wet deposition [61]. However, no valid monitoring data related to these factors have been obtained so far, making it impossible to quantify their contribution ratios. This also represents a key direction that needs to be supplemented in future research.
In summary, the deterioration of Honghu Lake’s water quality was not caused by a single factor, but rather the comprehensive result of “unmet residual external pollution standards, dominant internal release, and collapsing ecological functions.” Improving water quality cannot be achieved solely by reducing external inputs. A coordinated governance framework must be established from three dimensions to promote the sustainable improvement of Honghu Lake’s water quality, which include precision interception of external pollution, targeted remediation of internal pollution, and reconstruction of ecological functions.

5. Conclusions and Prospects

This study systematically analyzed the spatiotemporal dynamics of agricultural structure, NPS pollution loads, and water quality in the FLB from 2010 to 2023, with a focus on exploring the response of the basin’s aquatic ecosystem (with emphasis on Honghu Lake) to agricultural optimization. The key findings, limitations, future research directions, and policy implications are summarized as follows:
(1)
Conclusions and Recommendations
  • Compared with period 2010–2016, the AFMs of the FLB achieved significant optimization during 2016–2023. The proportion of RC area under sustainable increased by 14%, while the areas of DC with high fertilizer input and FA with high pollution risk decreased by 11% and 4%, respectively.
  • Optimization to the AFMs can significantly reduce N/P loads within the basin. This was specifically reflected in the following: NPS N/P loads decreased by 11–13%; N/P loads in the FLMC reduced by 22.94% and 3.15%, respectively; and the N/P inputs into Honghu Lake via the FLMC decreased synchronously by 22.04% and 2.02%. From 2016 to 2023, due to the lower N/P loads in the basin, the water quality of the FLMC was continuously optimized, with the TN/TP concentrations decreasing by approximately 2 mg/L and 0.04 mg/L.
  • Despite the reduction in external pollution, the TN/TP concentrations in Honghu Lake still increased by 0.46 mg/L and 0.06 mg/L, respectively. This reflected the constraints from climate variability, natural disasters, internal nutrient release from sediments, and the decline of water environment carrying capacity, highlighting the complexity of lake ecological restoration.
  • The pollution control and water environment improvement of the FLB and Honghu Lake can be advanced through three key measures: first, establish an agricultural pollution control system of “source control + process regulation” and promote green agricultural technologies; second, conduct comprehensive pollution prevention and control centered on “external pollution prevention and internal pollution treatment”; third, enhance water environment carrying capacity via the dual-path of “controlling pollution input, and increasing environmental capacity”.
(2)
Limitations and Prospects
  • The study focused on the impact of changes in agricultural models on the water quality of rivers and lakes, with insufficient consideration of other environmental factors such as atmospheric deposition and climate change. This led to an incomplete analysis of the causes of water quality deterioration in Honghu Lake. It will be necessary to systematically quantify the contribution weights of various factors to the water quality changes in Honghu Lake in the future.
  • Dynamic models such as the Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-Fortran (HSPF) were not integrated. As a result, the long-term responses of the ecosystem to future agricultural policies cannot be simulated, which limited the predictive ability of the research results.

Author Contributions

Y.H.: Software, Data curation, Visualization, Writing—original draft. Z.W.: Conceptualization, Methodology, Visualization. D.S.: Funding acquisition. R.L. and W.Z.: Methodology, Investigation. M.L., K.S. and X.C.: Investigation, Data collection. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by The Joint Funds of the National Natural Science Foundation of China (U21A20156; U2340219).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Chao, X.; Witthaus, L.; Bingner, R.; Jia, Y.; Locke, M.; Lizotte, R. An integrated watershedand water quality modeling system to study lake water qualityresponses to agricultural management practices. Environ. Model. Softw. 2023, 164, 105691. [Google Scholar] [CrossRef]
  2. Motew, M.; Chen, X.; Booth, E.G.; Carpenter, S.R.; Pinkas, P.; Zipper, S.C.; Loheide II, S.P.; Donner, S.D.; Tsuruta, K.; Vadas, P.A.; et al. The influence of legacy Fon lake water quality in a midwestern agricultural watershed. Ecosystems 2017, 20, 1468–1482. [Google Scholar] [CrossRef]
  3. Rodriguez, H.G.; Popp, J.; Maringanti, C.; Chaubey, I. Selection and placement of best management practices used to reduce water quality degradation in Lincoln Lake watershed. Water Resour. Res. 2011, 47, W01507. [Google Scholar] [CrossRef]
  4. Shi, Y.; Feng, L.; Gong, J. Four decades of the morphological dynamics of the lakes in the Jianghan Plain using Landsat observations. Water Environ. J. 2017, 31, 353–359. [Google Scholar] [CrossRef]
  5. Peng, X.S.; Fu, K.J. The Current Situation of Environmental Quality Surround Four Main Canal and Prevention Measures. J. Hunan Inst. Sci. Technol. (Nat. Sci.) 2015, 28, 77–80. [Google Scholar]
  6. Ma, Y.B.; Chen, L.W.; Liu, J.J.; Deng, C.Z.; Wu, Z.; Zhang, Y.Z.; Dai, J. Investigation of Agricultural Non-Point Source Pollution and Estimation of Pollution Loads in Honghu Valley. Hubei Agric. Sci. 2013, 50, 8–14. [Google Scholar]
  7. Zhou, H.E.; Chen, X.F.; He, X.; Ning, Z.P.; Wu, C.X. Research on Spatio-temporal Variation and Causes of Nutrients in Honghu Lake. Earth Environ. 2021, 49, 9–17. [Google Scholar]
  8. Wang, S.M. Analysis on Temporal-Spatial Variations in Water Quality and Pollution Sources of Honghu Lake. Master’s Thesis, Hubei University, Wuhan, China, 2018. [Google Scholar]
  9. Liu, Y.J.; Huang, S.Z.; Zhang, L.; Peng, X.; Zhang, X.Y.; Ge, F.J.; Liu, B.Y.; Wu, Z.B. Spatial Distribution Characteristics of Phosphorus Fractions and Release Risk in Sediments of Honghu International Importance Wetland. Environ. Sci. 2021, 42, 3198–3205. [Google Scholar]
  10. Min, M.; Li, H.; Miao, C.H. Output response of non-point source pollutants and its driving mechanisms of different arable land use patterns. Land Degrad. Dev. 2023, 34, 4123–4137. [Google Scholar] [CrossRef]
  11. Shi, J.H.; Jin, R.; Zhu, W.H. Quantification of effects of natural geographical factors and landscape patterns on non-point source pollution in watershed based on geodetector: Burhatong River Basin, Northeast China as an example. Chin. Geogr. Sci. 2022, 32, 707–723. [Google Scholar] [CrossRef]
  12. Zhang, G.Q.; Qin, X.Y.; Liao, J.F.; Liu, Q.M.; Huang, Y.F. Identification and regulation of critical source areas of non-point source pollution in medium and small watersheds based on source-sink theory. Land 2021, 10, 668. [Google Scholar]
  13. Wang, B.Y.; Wang, L.; Chen, J.C.; Qi, Q.; He, S.Y.; Yang, Z.Q.; Li, Z.H.; Li, K. Identification of ecological functional zoning and its influencing factors in the Sihu Lake Basin, China. Chin. J. Appl. Ecol. 2023, 34, 2757–2766. [Google Scholar]
  14. Yang, Q.X.; Li, W.C. Environmental changes since foundation of Pen-fish-farming in East Taihu Lake. China Environ. Sci. 1996, 2, 101–106. [Google Scholar]
  15. Hu, X.Y. Changes of water environment of Honghu Lake from 1990 to 2003. Wetl. Sci. 2006, 4, 115–119. [Google Scholar]
  16. Chen, S.; Mo, C.F.; Li, Y.Q.; Wu, D.N.; Zhong, Y.; Li, Z.H. Spatio-temporal characteristics of water quality and the pollution driving forces of Honghu Lake. Environ. Pollut. Control 2019, 41, 421–425. [Google Scholar] [CrossRef]
  17. He, Y.F.; Li, H.C.; Zhu, Y.J.; Yang, D.G. Status and spatial-temporal variations of eutrophication in Lake Changhu, Hubei Province. J. Lake Sci. 2015, 27, 853–864. [Google Scholar] [CrossRef]
  18. Ban, X.; Du, Y.; Wu, Q.Z.; Yu, C.; Feng, Q.; Wu, S.J. Spatial distrubution of water quality and pollutant source analysis in Sihu Basin. Resour. Environ. Yangze Basin 2011, 20, 112–116. [Google Scholar]
  19. Cao, C.G.; Jiang, Y.; Wang, J.; Yuan, P.; Chen, S. “Dual character” of rice-crayfish culture and strategy for its sustainable development. Chin. J. Eco-Agric. 2017, 25, 1245–1253. [Google Scholar]
  20. Hu, N.J.; Liu, C.H.; Chen, Q.; Zhu, L.Q. Life cycle environmental impact assessment of rice-crayfish integrated system: A case study. J. Clean. Prod. 2020, 280, 124440. [Google Scholar] [CrossRef]
  21. Chen, L.; Fan, X.P.; Huang, M.; Liu, D.; Wu, M.Y.; Zhang, F.; Zhang, Z.; Ni, C.; Cheng, Z. Characteristics of nitrogen and phosphorus loss in surface runoff under the rice-crawfish rotation system in the Jianghan Plain, China. J. Agro-Environ. Sci. 2022, 41, 1520–1530. [Google Scholar]
  22. Miao, J.J.; Liu, Y.F.; Hu, H.X.; Tu, R.F.; Zhan, R.C.; Xue, Z.J.; Xu, Q.R. Effects of Different Fertilization Modes on Nitrogen and Phosphorus Loss and Yield in Paddy Fields. J. Soil Water Conserv. 2020, 34, 86–93. [Google Scholar]
  23. Shi, X.L.; Zhang, W. Nitrogen and Phosphorus Variation Characteristics in Water and Soil of the Rice-Crayfish System. Jiangxi Sci. 2021, 39, 805–809. [Google Scholar]
  24. Tang, W.; Chen, C.; Huang, H. Effects of Different Rice Cultivation Patterns on Soil and Water Physical and Chemical Properties and Rice Yield. Crop Res. 2021, 35, 490–495. [Google Scholar]
  25. Si, G.H.; Yuan, J.F.; Peng, C.L.; Zhao, S.J.; Jia, P.A.; He, J.H. The impact of crayfish farming on water environment under rice shrimp co cultivation mode. Jiangsu Agric. Sci. 2019, 47, 299–303. [Google Scholar]
  26. Wang, H.; Shao, Q.; Li, R.; Song, M.; Zhou, Y. Governmental policies drive the LUCC trajectories in the Jianghan Plain. Environ. Monit. Assess. 2013, 185, 10521–10536. [Google Scholar] [CrossRef] [PubMed]
  27. Li, Y.; Wang, H.; Deng, Y.; Liang, D.; Li, Y.; Gu, Q. Applying water environment capacity to assess the non-point source pollution risks in watersheds. Water Res. 2023, 240, 120092. [Google Scholar] [CrossRef]
  28. State Environmental Protection Administration. Monitoring and Analysis Methods for Water and Wastewater, 4th ed.; China Environmental Science Press: Beijing, China, 2002.
  29. Wang, X.L.; Wang, H.L.; Wang, Q.M. On the hydrological response to the Land Use/Cover Change of Four-lake Basin in Jianghan Plain. J. HuaZhong Norm. Univ. (Nat. Sci.) 2014, 48, 101–105. [Google Scholar]
  30. Beaulac, M.N.; Reckhow, K.H. An examination of land use-nutrient export relationships. Water Resour. Bull. 1982, 18, 1013–1024. [Google Scholar] [CrossRef]
  31. Frink, C.R. Estimating nutrient exports to estuaries. J. Environ. Qual. 1991, 20, 717–724. [Google Scholar] [CrossRef]
  32. Gou, T.; Pei, D.F.; Liang, R.C.; She, L.; Yang, J.; Ma, Q.L. Analysis of nitrogen and phosphorus emissions from agricultural non-point sources based on improved output coefficient method. China Environ. Sci. 2023, 43, 6539–6550. [Google Scholar]
  33. Tao, L.; Peng, L.; Dai, L.L.; Yang, Z.; Chen, S.Y.; Ke, Y. Characteristics of non-point source pollution emission of rice-crayfish rotation system and its emergy analysis. J. Lake Sci. 2023, 35, 168–180. [Google Scholar]
  34. Dai, J.; Li, C.L.; Deng, C.Z.; Wu, Z.; Yang, L.Q. Investigation of Nitrogen and Phosphorus Pollution and Their Pollution Loads of Pond Crab Farming in Honghu Valley. Environ. Sci. Technol. 2010, 33, 169–172. [Google Scholar]
  35. Liu, F.Y.; Xia, Y.; Huang, M.; Lei, Q.L.; Liu, H.B.; Fan, X.P. Characteristics of nitrogen and phosphorus losses from farmlands through surface runoff under different cropping patterns in the Three Gorges Reservoir area of Hubei Province. J. Agric. Resour. Environ. 2018, 35, 550–558. [Google Scholar]
  36. Zhang, Y.Y.; Tan, X.; Xu, J.G.; Li, F.M. Spatial—Temporal distribution of agricultural non-point source pollution load in Xiantao City, Hubei Province. Yangtze River 2020, 51, 55–61. [Google Scholar]
  37. Gu, S.; Li, S. Riparian habitat quality as an indicator of land use/land cover effects on riverine water quality. Geogr. Sustain. 2024, 5, 135–143. [Google Scholar] [CrossRef]
  38. Yi, F.; Chen, M.; He, X.F.; You, J.H.; An, R.D. Effect of spatial scale on water quality in watershed land use analysis. China Environ. Sci. 2023, 43, 4280–4291. [Google Scholar]
  39. Zhang, X.; Qi, Y.; Li, H.; Wang, X.; Yin, Q. Assessing the response of non-point source nitrogen pollution to land use change based on SWAT model. Ecol. Indic. 2024, 158, 111391. [Google Scholar] [CrossRef]
  40. Nepal, D.; Parajuli, P.B.; Ouyang, Y.; To, S.F.; Wijewardane, N. Assessing hydrological and water quality responses to dynamic landuse change at watershed scale in Mississippi. J. Hydrol. 2023, 625, 129983. [Google Scholar] [CrossRef]
  41. Li, Y.Y.; Li, X.; Meng, C.; Wu, J.S. Analysis of agricultural non-point source pollution issue in waters and technical strategy of comprehensive prevention and control in rural area of China. Res. Agric. Mod. 2021, 42, 185–197. [Google Scholar]
  42. Liu, X.D. Evolution of Temporal and Spatial Pattern of Rice and Crayfish Fields in Qianjiang City and Its Impact on Ecological Environment. Master’s Thesis, Cental China Normal University, Wuhan, China, 2021. [Google Scholar]
  43. Du, F.; Hua, L.; Zhai, L.; Zhang, F.; Fan, X.; Wang, S.; Liu, Y.; Liu, H. Rice-crayfish pattern in irrigation-drainage unit increased N runoff losses and facilitated N enrichment in ditches. Sci. Total Environ. 2022, 848, 157721. [Google Scholar] [CrossRef]
  44. Yang, D.; Yang, Y.; Xia, J. Hydrological cycle and water resources in a changing world: A review. Geogr. Sustain. 2021, 2, 115–122. [Google Scholar] [CrossRef]
  45. Li, W.; Lü, S.S.; Zhao, Z.L. Impact of land use change on watershed water conservation and water quality purification service:a case study of Wujiang River Basin. Acta Ecol. Sin. 2023, 43, 8375–8389. [Google Scholar]
  46. Li, L.; Knapp, J.L.A.; Lintern, A.; Ng, G.H.C.; Perdrial, J.; Sullivan, P.L.; Zhi, W. River water quality shaped by land–river connectivity in a changing climate. Nat. Clim. Change 2024, 14, 225–237. [Google Scholar] [CrossRef]
  47. Tian, P.; Xu, Z.; Fan, W.; Lai, H.; Liu, Y.; Yang, P.; Yang, Z. Exploring the effects of climate change and urban policies on lake water quality using remote sensing and explainable artificial intelligence. J. Clean. Prod. 2024, 475, 143649. [Google Scholar] [CrossRef]
  48. Li, K.; Wang, L.; Li, Z.H.; Wang, X.R.; Chen, H.B.; Wu, Z.; Zhu, P. Spatial Variability Characteristics of Water Quality and Its Driving Forces in Honghu Lake During High Water- level Period. Environ. Sci. 2015, 36, 1285–1292. [Google Scholar]
  49. Deng, Y.J.; Zhou, S.H.; Yang, C.H.; Xia, Z.; Liu, K. Analysis of drought and flood change based on standard precipitation index (SPI) and its impact on lake water level of Honghu lake. Hubei Agric. Sci. 2020, 59, 32–37. [Google Scholar]
  50. Wang, M.; Liu, M.; Xia, Z.H. Effect of future climate change on water resource in Honghu watershed based on SWAT model. J. Meteorol. Environ. 2016, 32, 36–47. [Google Scholar]
  51. Li, D.; Bu, S.; Li, Q.; Chen, S.; Zhen, Z.; Fu, C. Water environment capacity estimation and pollutant reduction of Yanghe Reservoir Basin in Hebei Province, China, via 0-D water quality model. Environ. Earth Sci. 2021, 80, 508. [Google Scholar] [CrossRef]
  52. Yu, H.B.; Xi, B.D.; Guo, X.J.; Zhai, L.H.; He, L.S.; Xu, Q.G.; Liu, H.L. Effect of Rainfall Runoff on Nitrogen and Phosphorus Loss in Farming Drainage Ditch. Res. Environ. Ment. Sci. 2009, 22, 409–414. [Google Scholar]
  53. Liang, L.Q.; Guo, C.X.; Chen, X.H.; Li, J.W.; Chen, C.; Yang, L.P.; Shen, G.X. Characteristics of pollution discharges during pond cleaning in typical fish ponds in Taihu Basin and its impacts on water environment. J. Lake Sci. 2023, 35, 181–191. [Google Scholar]
  54. Wen, J.W.; Wang, P.; Huang, G.X.; Zhang, H.; Nie, M.H.; Ding, M.J.; Zhe, Y.Y. Influence of Land Use Structure and Spatial Pattern on Water Quality of Small and Medium-sized Rivers in Poyang Lake Basin. Environ. Sci. 2023, 44, 6728–6743. [Google Scholar]
  55. Chen, X.; Dai, Z.S.; Jiang, L.; Ye, C.; Wang, Y.H.; Huang, X.L.; Yang, C.C.; Chen, S.D. Simulation of nitrogen export scenarios in Chaohu Basin based on land use patterns. J. Lake Sci. 2024, 36, 149–164. [Google Scholar] [CrossRef]
  56. Xu, Y.; Feng, L.; Hou, X.J.; Wang, J.; Tang, J. Four-decade dynamics of the water color in 61 large lakes on the Yangtze Plain and the impacts of reclaimed aquaculture zones. Sci. Total Environ. 2021, 781, 146688. [Google Scholar] [CrossRef] [PubMed]
  57. Zhou, W.C.; Shi, Y.H.; Pan, L. Current Status and Controlling Strategies of Water Pollution in Honghu Lake Wetland in Jianghan Plain of Middle Reaches of Yangtze River. Wetl. Sci. Manag. 2019, 15, 31–34. [Google Scholar]
  58. Liu, X.; Li, E.H.; Xu, J.; Deng, Z.L.; Huang, X.L.; Wang, Y.C.; Wang, X.L. Evolution mechanism of Lake Honghu wetland ecosystem and regime shift crucial threshold. J. Lake Sci. 2023, 35, 934–940. [Google Scholar]
  59. Zhang, X.W.; Peng, X.; Deng, Z.L.; Ge, F.L.; Zhang, H.K.; Wang, L.J.; Fang, M.J.; Xiong, Y.F.; Zhang, L.; Li, X.; et al. Spatio-temporal characteristics and causes of eco-environmental changes in Lake Honghu from 2019 to 2023. Lake Sci. 2025, 37, 496–507. [Google Scholar]
  60. Pang, R.H.; Zhu, Y.H.; Lv, H.S.; Chen, Y.N. Effect of Exogenous Non-point Source Pollution on Honghu with the Corresponding Protection and Restoration Countermeasures. Water Resour. Power 2016, 34, 56–59. [Google Scholar]
  61. Wei, D.X.; Li, X.; Zhao, Y.H. Analyses of atmospheric nitrogen and phosphorus deposition at Hefei Science Island and its impact on Chaohu Lake. J. Hefei Univ. Technol. 2018, 41, 125–1266. [Google Scholar]
Sustainability 17 08838 g001
Figure 1. Study Area Zoning and Monitoring Point Distribution Map.
Figure 1. Study Area Zoning and Monitoring Point Distribution Map.
Sustainability 17 08838 g001
Sustainability 17 08838 g002
Figure 2. The water system and flow direction in the FLB.
Figure 2. The water system and flow direction in the FLB.
Sustainability 17 08838 g002
Sustainability 17 08838 g003
Figure 3. Land use (a,b) and agricultural cultivation models (c) of the FLB from 2010 to 2023.
Figure 3. Land use (a,b) and agricultural cultivation models (c) of the FLB from 2010 to 2023.
Sustainability 17 08838 g003
Sustainability 17 08838 g004
Figure 4. The dynamics of NPS N/P-loads during period 1 and period 2.
Figure 4. The dynamics of NPS N/P-loads during period 1 and period 2.
Sustainability 17 08838 g004
Sustainability 17 08838 g005
Figure 5. N-loads (a) and P-loads (b) from different sources within basin from 2010 to 2023.
Figure 5. N-loads (a) and P-loads (b) from different sources within basin from 2010 to 2023.
Sustainability 17 08838 g005
Sustainability 17 08838 g006
Figure 6. Characteristics of changes in water quantity and quality in the FLMC (a) and Hong Lake (b) under the transformation of agricultural farming patterns.
Figure 6. Characteristics of changes in water quantity and quality in the FLMC (a) and Hong Lake (b) under the transformation of agricultural farming patterns.
Sustainability 17 08838 g006
Sustainability 17 08838 g007
Figure 7. Intra-annual variation characteristics of water quantity and quality in the FMLC (a) and Hong Lake (b) in 2022. Note: Different letters indicate significant differences (p < 0.05).
Figure 7. Intra-annual variation characteristics of water quantity and quality in the FMLC (a) and Hong Lake (b) in 2022. Note: Different letters indicate significant differences (p < 0.05).
Sustainability 17 08838 g007
Sustainability 17 08838 g008aSustainability 17 08838 g008b
Figure 8. (a,b) The correlation heat of TN/TP concentrations in FLMC and N/P-loads of different types in Sihu Basin (Note: * represents a statistically highly significant correlation).
Figure 8. (a,b) The correlation heat of TN/TP concentrations in FLMC and N/P-loads of different types in Sihu Basin (Note: * represents a statistically highly significant correlation).
Sustainability 17 08838 g008aSustainability 17 08838 g008b
Sustainability 17 08838 g009
Figure 9. The TN/TP concentrations in FLMC and Honghu Lake from 2017 to 2023.
Figure 9. The TN/TP concentrations in FLMC and Honghu Lake from 2017 to 2023.
Sustainability 17 08838 g009
Sustainability 17 08838 g010
Figure 10. The spatial variation characteristics of TN (a) and TP (b) concentration in the FMLC.
Figure 10. The spatial variation characteristics of TN (a) and TP (b) concentration in the FMLC.
Sustainability 17 08838 g010
Table 2. The change characteristics of N/P-loads under the adjustment of AFMs from 2016 to 2023 in the FLB.
Table 2. The change characteristics of N/P-loads under the adjustment of AFMs from 2016 to 2023 in the FLB.
LLPN-Load (t/a)Load Proportion (%)P-Load (t/a)Load Proportion (%)
AFM 20162023201620232016202320162023
FA9120.025505.3638.325.92416.441471.3258.040.5
DC9382.836735.6439.431.6847.37606.4820.316.7
RCF5282.949045.3122.242.5905.651551.3721.742.8
Note: Load and Load Proportion, LLP.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hu, Y.; Wang, Z.; Shao, D.; Li, R.; Zhang, W.; Long, M.; Song, K.; Cao, X. Effects of Agricultural Production Patterns on Surface Water Quality in Central China’s Irrigation Districts: A Case Study of the Four Lakes Basin. Sustainability 2025, 17, 8838. https://doi.org/10.3390/su17198838

AMA Style

Hu Y, Wang Z, Shao D, Li R, Zhang W, Long M, Song K, Cao X. Effects of Agricultural Production Patterns on Surface Water Quality in Central China’s Irrigation Districts: A Case Study of the Four Lakes Basin. Sustainability. 2025; 17(19):8838. https://doi.org/10.3390/su17198838

Chicago/Turabian Style

Hu, Yanping, Zhenhua Wang, Dongguo Shao, Rui Li, Wei Zhang, Meng Long, Kezheng Song, and Xiaohuan Cao. 2025. "Effects of Agricultural Production Patterns on Surface Water Quality in Central China’s Irrigation Districts: A Case Study of the Four Lakes Basin" Sustainability 17, no. 19: 8838. https://doi.org/10.3390/su17198838

APA Style

Hu, Y., Wang, Z., Shao, D., Li, R., Zhang, W., Long, M., Song, K., & Cao, X. (2025). Effects of Agricultural Production Patterns on Surface Water Quality in Central China’s Irrigation Districts: A Case Study of the Four Lakes Basin. Sustainability, 17(19), 8838. https://doi.org/10.3390/su17198838

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop