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Article

How Land Use and Hydrological Characteristics Impact Stream Conditions in Impaired Ecosystems

by
Se-Rin Park
1,†,
Yujin Park
1,†,
Jong-Won Lee
1,
Hyunji Kim
2,
Kyung-A You
2 and
Sang-Woo Lee
1,*
1
Department of Forestry and Landscape Architecture, Konkuk University, Seoul 05029, Republic of Korea
2
Water Environmental Engineering Research Division, Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2025, 14(4), 829; https://doi.org/10.3390/land14040829
Submission received: 26 February 2025 / Revised: 2 April 2025 / Accepted: 7 April 2025 / Published: 10 April 2025
(This article belongs to the Special Issue The Mechanism of Land-Water Interaction and Management Strategies: Recent Discoveries and the Way Forward)
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Abstract

:
Anthropogenic influence has altered watershed environments and hydrological processes, leading to increased occurrences of impaired streams and negative impacts on benthic invertebrates. While individual environmental factors affecting benthic macroinvertebrates have been studied, the cascading effects of land use change and hydrological alterations remain unclear. This study employed structural equation modeling (SEM) to analyze the interactions among land use proportion, hydrological characteristics, substrate composition, and water quality and their influence on benthic macroinvertebrate communities in impaired streams upstream of the Paldang Dam in the Han River Basin, South Korea. Analysis of data from 24 streams surveyed between 2018 and 2022—3 or 6 streams per year—under the Impaired Stream Diagnosis Program indicated that urban and agricultural land cover, low substrate diversity, high pollutant concentrations, and altered flow conditions (low velocity and discharge) were associated with decreased pollution-sensitive Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa and increased pollution-tolerant and collector–gatherer taxa. These findings highlight the role of land use-driven hydrological changes in stream ecosystem degradation and underscore the need for targeted restoration strategies, such as riparian buffer zones, substrate enhancement, and hydrological flow restoration, to mitigate these impacts and improve benthic macroinvertebrate habitats.

1. Introduction

Stream ecosystems provide essential services such as water purification, flood regulation, and habitat support for biodiversity and human communities [1,2,3]. However, widespread anthropogenic activities—particularly land use changes driven by urbanization, agriculture, and industrialization—have significantly altered these ecosystems, resulting in water quality degradation, hydrological disruptions, and biodiversity loss [4,5,6]. Given that stream ecosystems function as dynamic systems shaped by complex interactions between biotic and abiotic factors, understanding how land use changes lead to ecosystem degradation is essential for effective management and restoration [7]. Without targeted interventions, these stressors may continue to degrade stream ecosystems, leading to increasingly severe and persistent disturbances [8,9]. Therefore, a systematic and data-driven approach is needed to identify the key factors causing degradation. Since conducting experimental studies to determine the relative importance of each stressor is often prohibitively expensive and logistically challenging, data-driven methods offer a practical alternative. This approach will help develop sustainable restoration strategies, policies, and management practices to reduce the impacts of land use change.
Stream ecosystems are increasingly degraded due to various anthropogenic activities [10,11,12], and are further aggravated by habitat destruction from stream straightening, the construction of artificial structures, and the invasion of alien species. Impairments in stream ecosystems manifest in multiple forms depending on the specific disturbance factors and affected components, including physical structure degradation, water quality deterioration, biological impairment, and hydrological disruptions [13]. Notably, degradation in one aspect of a stream ecosystem often triggers cascading effects, leading to a compounding cycle of ecological decline [14,15]. Given these complexities, the timely and effective management of impaired streams is crucial. The U.S. Environmental Protection Agency (EPA) has introduced the Casual Analysis/Diagnosis Decision Information System (CADDIS), which aids in identifying the causes of stream impairment and evaluating the effectiveness of restoration efforts [16]. Similarly, Australia’s National Research Institute, E-Water, has developed the Eco Evidence program, which systematically analyzes degradation drivers to support evidence-based stream management decisions [17]. In South Korea, the Stream Impairment Diagnosis system has been implemented to assess degraded streams and propose targeted restoration and management strategies to mitigate the underlying causes of ecosystem degradation [18]. Although these diagnostic approaches support the development of effective restoration policies in response to land use changes, they may also lead to overly standardized and bureaucratic restoration practices that fail to account for local specificity and ecological complexity adequately.
Although biological assessment approaches are not necessarily essential for identifying impaired streams, these biological indicators integrate the cumulative impacts of multiple stressors over time, offering a more holistic perspective on ecosystem integrity than physical or chemical assessments [18,19,20]. Extensive research has examined the complex relationships between benthic macroinvertebrates and stream environmental factors such as water quality, flow dynamics, and substrate composition. Anthropogenic influences, particularly those associated with land use changes, significantly impact these environmental conditions, thereby altering benthic macroinvertebrate communities. Rai et al. [21] supported the fact that human-induced water pollution modifies species composition, leading to a decline in pollution-sensitive taxa, with water quality parameters playing a particularly influential role in Olarong Chhu and Paa Chhu rivers in western Bhutan.
In addition to water quality, factors like flow volume and velocity affect sediment transport, changing the physical structure of the streambed and affecting water temperature and quality, which in turn impacts the macroinvertebrate composition [22]. An increase in fine sediment deposition has been shown to reduce the richness and abundance of Ephemeroptera, Plecoptera, and Trichoptera (EPT) species, while sediment nutrient content alters water nutrient concentrations, playing a key role in structuring macroinvertebrate communities [23]. Watershed-scale environmental factors, particularly land use changes associated with urbanization and agricultural expansion, introduce anthropogenic stressors that degrade water quality, alter streambed conditions, and modify flow regimes, ultimately impacting benthic macroinvertebrate communities both directly and indirectly [13,24].
While many studies have examined individual environmental factors influencing benthic macroinvertebrates, most have focused on their unidirectional effects, often overlooking the complex interactions among multiple variables [21,25,26]. As a result, the intricate relationships among land use, water quality, hydrological characteristics, and habitat conditions remain insufficiently understood. To address this gap, this study employs structural equation modeling (SEM), a robust analytical tool for capturing complex interdependencies, to assess how water quality, substrate composition, hydrological conditions, and land use intensity collectively shape benthic macroinvertebrate communities in impaired streams within the Han River Basin. By specifically focusing on degraded streams, this study provides critical insights into key degradation drivers affecting aquatic ecosystems and offers valuable guidance for targeted stream restoration and management strategies in response to land use change.

2. Materials and Methods

2.1. Impaired Stream Diagnosis Program by Korean Ministry of Environment (MOE)

Since 2008, the Korean Ministry of Environment (MOE) has operated the National Aquatic Ecological Monitoring Program (NAEMP) to assess the current state of aquatic ecosystems, evaluate the effectiveness of key environmental policies, and collect foundational data for policy formulation by monitoring 3035 sampling sites across the nation’s rivers and streams. The NAEMP conducts biannual surveys at monitoring points nationwide, evaluating habitat conditions using the habitat and riparian index and the riparian vegetation index as well as biochemical conditions and biological community integrity. Biological community assessment includes indices such as the trophic diatom index, the benthic macroinvertebrate index (BMI), and the fish assessment index for rivers and streams.
In addition to the monitoring program, South Korea has been implementing the Ecological Stream Restoration Project since 2009, which focuses on restoring and managing aquatic ecosystems. This project has targeted an average of 30 streams per year. However, due to the absence of a diagnostic process to identify the exact causes of degradation, these restoration efforts have often failed to remove the root causes of impairment, resulting in ongoing negative impacts even after the completion of the projects, thereby reducing their effectiveness. The MOE established a Stream Impairment Diagnosis system to improve the success of stream restoration and management projects in 2019. The MOE defines an impaired stream as one where the structure or function has deteriorated due to natural or artificial disturbances, causing abnormal changes from its original state. In the monitoring program, each biological index is rated on a scale from A to E, with a stream rated D or below classified as impaired. Based on these monitoring data, the MOE catalogs impaired streams and selects those most in need of restoration. A diagnosis is then conducted as per the national manual. Field surveys are carried out twice a year, in spring and fall, when stream variability is at its lowest, to identify the primary causes of impairment and propose restoration measures.

2.2. Study Streams and Sampling Sites

The study area, the Han River Basin, is one of South Korea’s four major river basins, covering an area of 41,947 km2, which accounts for a quarter of the country (Figure 1). The basin spans five administrative regions—Seoul, Incheon, Gyeonggi-do, Gangwon-do, and Chungcheong-do. Notably, the Paldang Dam, a crucial source of water supply for Seoul and the surrounding metropolitan area, is located within the basin. The upstream regions of Paldang Dam, including the watershed and streams, are vital for protecting the water source of the Han River. Accordingly, from 2018 to 2022, diagnostics were conducted on 24 impaired streams (three streams each in 2018 and 2022, and six streams annually from 2019 to 2021) located in the upstream region of the Paldang Dam in the Han River Basin, based on the diagnostic system of the MOE. These streams were selected in consultation with the Han River Basin Management Committee, considering criteria such as the degree of stream and biota impairment, stream size, and basin characteristics, in alignment with the foundational plan for ecological stream restoration. For the selected impaired streams, between 5 and 10 monitoring sites were designated for each stream, depending on stream size and segment characteristics, resulting in a total of 180 monitoring sites. These sites were surveyed twice annually, in spring and fall, yielding a total of 360 survey data points. Outliers and missing values primarily resulted from field measurement errors or equipment malfunctions and were not randomly distributed across variables. Given their relatively low proportion, these data points were excluded from the analysis to ensure consistency across variables, resulting in a final dataset of 331 observations.
To address potential spatial and temporal autocorrelation, we incorporated specific design considerations into our sampling strategy, although these factors were not explicitly modeled in the statistical analysis. Spatial autocorrelation was minimized by selecting sites with distinct stream and watershed characteristics, thereby reducing the likelihood of spatial dependence among observations. To mitigate temporal variation, field sampling was conducted during stable hydrological periods that represent typical stream conditions and avoid extreme events such as rainfall-driven disturbances. While these efforts may not completely eliminate autocorrelation, they were intended to reduce its influence on the results and improve the robustness of the analysis.

2.3. Selected Variables

To understand the complex relationships between watershed and stream environments and biological communities, we utilized variables related to water quality, substrate composition, hydrological characteristics, and watershed land use from the data collected between 2018 and 2022. Water quality indicators included biochemical oxygen demand (BOD), COD, TN, TP, and turbidity. Hydrological characteristics comprised flow volume and velocity, while substrate composition included substrate embeddedness and the ratio of silty sand, gravel, and bedrock. Land use proportion was determined from the ratio of land cover (urban and agricultural area) within each impaired stream’s sub-watershed, obtained from the land cover map of the MOE, and calculated using ArcGIS 10.6.1 [27].
The data on benthic invertebrate communities used in this study include the total number of benthic invertebrate communities, the density ratio of EPT taxa, the density ratio of tolerant taxa, the density ratio of collector–gatherer taxa, and the BMI grades, which serve as criteria for determining the impairment level of benthic invertebrate communities in the Impaired Stream Diagnosis Program. The total number of benthic invertebrate communities represents species diversity, the density of EPT taxa and collector–gatherer taxa signifies species composition, and the density of tolerant taxa reflects the impact of environmental disturbance. In addition, a high proportion of EPT taxa indicates a healthy stream environment, while a high proportion of collector–gatherer taxa suggests a stream with significant organic matter input and slower flow rates. A high proportion of tolerant taxa signifies a disturbed or polluted environment [18]. The BMI is a composite metric calculated by integrating various species of benthic macroinvertebrates, taking into account the presence of specific indicator species, species indicator weight values, and saprobic indices. Thus, the BMI not only reflects the overall condition of the stream ecosystem and species diversity but also considers the environmental tolerance of each species [19]. In this study, for ease of analysis, the BMI grades (A to E) were converted into a numeric scale, with A assigned a value of 5, B a value of 4, and so on, down to E as 1.

2.4. Structural Equation Model (SEM)

The SEM is an analytical method that integrates aspects of factor analysis, regression analysis, and path analysis to assess causal relationships and correlations among multiple variables within a single model [28]. The SEM is capable of estimating the relationships between variables by incorporating latent, observed, and error variables [29]. Latent variables in the SEM are estimated through observed variables, and the direct and indirect influences on the dependent variable are assessed by utilizing path coefficients derived from the combined effects of similar observed variables. Aquatic ecology data, acquired through field surveys and potentially containing various errors, can benefit from the SEM, which considers errors in the presented results [30]. Particularly in aquatic ecosystems, where various factors such as hydrology, water quality, habitat, and biological elements exhibit complex interactions, the SEM proves to be valuable for analyzing intricate relationships, providing a more rational and reliable assessment of the data [31].
While structural equation modeling may not fully capture latent factors or unmeasured disturbances beyond the variables explicitly included in the model, it is an effective tool for quantitatively assessing the direct and indirect relationships among environmental factors (e.g., physical habitat, water quality, hydrology) that influence benthic macroinvertebrates. The SEM for this study was constructed based on previous empirical research and ecological understanding of the hierarchical relationships among environmental stressors [13,30,31,32]. From this initial model, an optimized model was developed by removing statistically non-significant paths to enhance model fit and improve the significance of relationships among variables. The final model was chosen after evaluating multiple alternatives to ensure both good model fit and ecological relevance. The model aimed to analyze the structural correlations of water quality, substrate composition, hydrological characteristics, and land use proportion with benthic invertebrate communities and BMI grades. As presented in Figure 2, e1 to e22 represent measurement errors, with water quality, substrate composition, hydrological characteristics, and land use proportion serving as latent variables.
The SEM estimates the model through the maximum likelihood method (MLE), and the path variable that shows the influence on each variable is judged to be significant when the Critical Ratio value is greater than ±1.965. MLE was chosen for this analysis because it is one of the most effective estimation methods, providing a variety of model fit indices, allowing flexible application to complex structural models, and enabling robust statistical inference under the assumption of multivariate normality. Among various indicators that evaluate the fit of an SEM, this study used the normed fit index (NFI), Tucker–Lewis index (TLI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). When NFI, TLI, and CFI are all greater than or equal to 0.90 or RMSEA is less than or equal to 0.10, a good model fit is indicated. The SEM analysis was conducted using IBM AMOS 28 Graphics.

3. Results

3.1. Descriptive Statistics

We conducted descriptive statistics to analyze the variables (Table 1). The average BMI grade was 3.33, corresponding to a C (“Average”) grade. The mean total number of benthic invertebrate communities was 13.06, with a mean density ratio of 34.07% for EPT taxa, 57.65% for tolerant taxa, and 73.12% for collector–gatherer taxa. According to the impairment criteria set by the MOE [18], which defines impairment as possessing a total number of benthic invertebrate communities below 10, a density ratio of EPT taxa below 35%, a collector–gatherer taxa ratio above 75%, and a tolerant taxa ratio above 60%, these values indicate a condition close to impairment. Flow volume and velocity ranged from 0.00 to 2.28 m3/min and 0.03 to 10.00 cm/s, respectively. The average concentrations for BOD, COD, TN, TP, and turbidity were 2.23 mg/L, 4.45 mg/L, 3.66 mg/L, 0.09 mg/L, and 26.09 NTU, respectively. BOD, COD, and TP concentrations are classified as “slightly good” under Article 2 of the Act on Environmental Policy. The average substrate embeddedness was 50.33%, with silty sand, gravel, and bedrock densities being 46.93%, 42.34%, and 8.56%, respectively, indicating a high proportion of silty sand and gravel in the substrate. The average urban area ratio was 11.85%, and the average agricultural area ratio was 22.26%.

3.2. Comprehensive Model for Benthic Macroinvertebrate Communities

3.2.1. Full Model

To construct a comprehensive SEM that accounts for the interactions among hydrological characteristics, water quality, land use proportion, and substrate composition, the relationships between each potential variable and benthic macroinvertebrate communities were analyzed (Figure 3, Table 2). In each SEM, the influence of hydrological characteristics, water quality, land use proportion, substrate composition, and benthic macroinvertebrate community indicators was found to be significant. Accordingly, the full model, as presented in Figure 2, was developed and analyzed. However, the results indicated that land use proportion, hydrological characteristics, and substrate composition did not have a significant effect on water quality. Additionally, land use intensity did not significantly influence hydrological characteristics. Water quality also demonstrated no significant direct impact on the density ratios of EPT taxa, collector–gatherer taxa, and tolerant taxa. To improve the SEM fit and enhance the significance of the relationships among variables, the full model was refined by removing the paths that represented non-significant relationships.

3.2.2. Optimization of the Model

An optimized model was developed by taking into account the statistically non-significant paths identified in the full model, and the relationships between each latent variable and benthic macroinvertebrate communities were subsequently analyzed (Figure 4, Table 3). Silty sand and substrate embeddedness, which were included as observed variables representing substrate composition, showed strong negative factor loadings, indicating a poor-quality substrate environment. Among the hydrological characteristics, flow velocity emerged as the most important factor, while in the case of water quality, BOD and COD were similarly significant. In terms of land use proportion, agricultural land use had the greatest impact, with a value of 0.51. In the interactions between latent variables, an increase in land use proportion was associated with increased water pollution concentration, while it negatively affected substrate composition. Conversely, hydrological characteristics—particularly higher flow velocity—positively influenced substrate composition by reducing the deposition of fine sediments and enhancing substrate diversity.
When evaluating the effects of latent variables on benthic invertebrate community indicators, land use proportion had the most significant impact on all indicators, followed by substrate composition, hydrological characteristics, and water quality (Figure 4, Table 3). An increased proportion of land use and the composition of substrates led to a decline in both the total number of benthic invertebrate communities and the density ratio of EPT taxa, while the density ratio of collector–gatherer and tolerant taxa rose. Furthermore, the decline in water quality (i.e., higher pollutant concentrations) also resulted in a reduction in the total number of benthic invertebrate communities and an increase in the density ratio of collector–gatherer taxa. However, the increase in flow velocity and discharge positively influenced the total number of benthic invertebrate communities and EPT taxa while reducing the density ratio of collector–gatherer taxa and tolerant taxa. The total number of benthic invertebrate communities had the greatest impact on BMI grades: an increase in the total number of benthic communities and a higher density ratio of EPT taxa resulted in higher BMI grades, while more collector–gatherer and tolerant taxa resulted in lower BMI grades.

3.2.3. Model Validation

Both the full and the optimized models were validated using the multiple goodness-of-fit criteria. Based on these fit indices, we confirmed that the optimized model was well-suited to explaining the data in this study (Table 4).

4. Discussion

4.1. Influence of Land Use and Hydrological Characteristics on Stream Ecosystems

The optimized SEM results indicate that land use proportion within watersheds exerts the most significant influence on benthic invertebrate communities, directly and indirectly shaping habitat conditions through hydrological alterations and pollutant influx [33,34,35]. Urban and agricultural land use contributes to increased point and non-point source pollution, degrading water quality and modifying streambed conditions. Pollutant runoff from urban and industrial areas introduces contaminants, while agricultural activities accelerate soil erosion and increase organic matter input through fertilizer runoff, raising the fine sediment ratio in the streambed [33,34,35]. Consistent with these findings, the present study revealed that increased land use intensity correlates with elevated pollutant concentrations and reduced substrate heterogeneity, ultimately deteriorating habitat conditions for benthic macroinvertebrates (Figure 4).
Impaired streams typically drain watersheds dominated by urban and agricultural land use, which frequently leads to degraded water quality and habitat instability due to their proximity to pollutant sources. This habitat degradation contributes to the decline in pollution-sensitive Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, while favoring pollution-tolerant groups such as collector–gatherers [36,37]. Carlson et al. [36] similarly found that watershed land use was a strong determinant of benthic invertebrate community composition, with agricultural inputs altering substrate structures and increasing the density of tolerant taxa. Likewise, Fergus et al. [37] demonstrated that land use changes significantly impacted bed stability and water quality, exerting the greatest influence on stream macroinvertebrate assemblages compared to other environmental variables.
In addition to land use, hydrological characteristics, including flow velocity and discharge, play a crucial role in shaping substrate conditions and overall habitat suitability. Adequate flow velocity sustains high oxygen levels and prevents excessive siltation, while stable discharge helps maintain diverse microhabitats [38,39]. Conversely, anthropogenic modifications such as dam and weir construction often reduce flow velocity, leading to sediment accumulation, organic matter deposition, and habitat homogenization, further disadvantaging sensitive macroinvertebrate taxa. These findings align with those of Lee et al. [40] and Townsend et al. [41], who reported that low flow velocities and fine sediment substrates facilitated the dominance of tolerant species such as oligochaetes and chironomids while reducing the abundance of flow-dependent taxa like EPT.

4.2. Effects of Other Environmental Factors on Sream Ecosystems

Beyond land use and hydrological changes, substrate composition and water quality emerged as key environmental determinants of benthic macroinvertebrate communities. Since macroinvertebrates inhabit streambeds and rely on geomorphological structures for feeding, reproduction, and refuge, substrate complexity plays a vital role in determining species composition [42]. This study found that gravel and cobble substrates supported higher benthic invertebrate diversity, whereas sandy and silty substrates, often associated with low flow velocity, resulted in reduced species richness [38,39].
The interaction between substrate composition and water quality is particularly important in impaired streams, where fine sediment accumulation from watershed disturbances alters substrate characteristics. Increased fine sediment deposition not only degrades substrate complexity but also influences water column nutrient concentrations, further affecting macroinvertebrate communities [43]. However, some studies have reported that sediments can have positive effects on water quality by acting as storage sites for pollutants such as metals and organic matter, or by helping in regulating water flow. Artificial structures such as weirs exacerbate this issue by reducing flow velocity, allowing fine sediments to settle and continuously accumulate on the streambed. This study confirmed that substrate embeddedness and sand–silt fractions significantly influenced benthic invertebrate communities, contributing to a decline in EPT taxa while promoting tolerant species and collector–gatherers.
Water quality factors, including total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD), also indirectly influence macroinvertebrate community composition by affecting the tolerance capacities of species [25]. Previous studies have shown that streams exposed to high nutrient loads and organic pollutants from agricultural and urban sources exhibit a decline in sensitive taxa and an increase in species adapted to degraded conditions [44]. Therefore, improving both substrate and water quality conditions is crucial for restoring benthic macroinvertebrate diversity in impaired streams.

4.3. Management and Restoration of Impaired Streams

Since land use changes greatly affect stream hydrology, water quality, and habitats, in order to improve benthic invertebrate communities, it is crucial to focus on reducing the impacts of urbanization and agriculture through effective land use planning at the watershed level. One key strategy is the designation of riparian buffer zones, which—although often limited in effect when narrow or implemented in isolation—can still contribute to stream health by reducing development pressure and maintaining existing vegetation. In Korea, such areas are often preserved or converted into green spaces through land use policies, which help prevent further degradation. When designed with multi-layered vegetative filters, these buffers can assist in reducing soil erosion, trapping sediments, and intercepting non-point source pollutants, thereby contributing to improved water quality and more stable habitat conditions [45,46]. Additionally, incorporating natural substrate materials such as gravel, cobbles, and woody debris can enhance substrate complexity, providing a more suitable habitat for macroinvertebrates [38,47]. The introduction of aquatic vegetation and eco-friendly engineering materials in degraded stream sections can also aid in habitat recovery and improve ecosystem resilience [46].
Hydrological restoration measures should focus on restoring natural flow regimes by evaluating artificial structures—particularly agricultural weirs—that are no longer functional or maintained. In rural areas where stream fragmentation caused by outdated or abandoned weirs reduces flow and disrupts connectivity, removing such non-functional structures can help restore hydrological continuity and improve habitat conditions for benthic macroinvertebrates [43]. Moreover, sustainable land use policies that minimize impervious surfaces and promote environmentally friendly agricultural practices are critical for maintaining hydrological balance and preventing further stream degradation.

5. Conclusions

This study employed structural equation modeling (SEM) to examine the complex interrelationships among watershed- and stream-scale environmental variables—such as substrate composition, water quality, hydrological characteristics, and land use proportion—and their collective effects on benthic macroinvertebrate communities in impaired streams within the Han River Basin. The findings highlight that land use proportion, which shapes both watershed and in-stream conditions, serves as the most influential factor, while habitat-quality-related variables, including substrate composition and hydrological characteristics, also play crucial roles in determining benthic macroinvertebrate community structure. To improve BMI grades and support healthier aquatic ecosystems, it is essential to mitigate the adverse effects of urban and agricultural land use, enhance substrate heterogeneity through diverse materials, reduce pollutant concentrations, and maintain optimal flow velocity and discharge levels. Such measures would contribute to increasing the total abundance of benthic invertebrates and the density ratio of sensitive Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa while reducing the dominance of collector–gatherer and pollution–tolerant species.
A systematic diagnostic approach is essential for evaluating impacted stream sections and identifying the main causes of their deterioration, ensuring effective restoration planning. This study provides valuable insights for managing impaired streams, especially in addressing land use-driven degradation. However, future research should focus on exploring more detailed habitat variables and establishing ecological thresholds within stream environments to improve restoration strategies. By defining key habitat criteria for benthic macroinvertebrates, such research would help refine targeted and effective stream restoration efforts.

Author Contributions

Conceptualization, S.-R.P. and S.-W.L.; Methodology, S.-R.P., J.-W.L. and S.-W.L.; Software, Y.P. and J.-W.L.; Validation, S.-R.P., Y.P. and J.-W.L.; Formal Analysis, S.-R.P., Y.P., J.-W.L. and S.-W.L.; Investigation, S.-R.P. and Y.P.; Data Curation, J.-W.L.; Writing—Original Draft Preparation, Y.P.; Writing—Review and Editing, S.-R.P.; Visualization, Y.P.; Supervision, S.-W.L.; Project Administration, H.K. and K.-A.Y.; Funding Acquisition, S.-W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The datasets presented in this article are not readily available because the rights to the data are owned by the government and are subject to restrictions.

Acknowledgments

This study was supported by Konkuk University, South Korea, in 2024.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Palmer, M.; Ruhi, A. Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration. Science 2019, 365, eaaw2087. [Google Scholar] [CrossRef] [PubMed]
  2. Yeakley, J.A.; Ervin, D.; Chang, H.; Granek, E.F.; Dujon, V.; Shandas, V.; Brown, D. Ecosystem services of streams and rivers. In River Science: Research and Management for the 21st Century; Wiley: Hoboken, NJ, USA, 2016; pp. 335–352. [Google Scholar]
  3. Veerkamp, C.J.; Schipper, A.M.; Hedlund, K.; Lazarova, T.; Nordin, A.; Hanson, H.I. A review of studies assessing ecosystem services provided by urban green and blue infrastructure. Ecosyst. Serv. 2021, 52, 101367. [Google Scholar] [CrossRef]
  4. Meyer, J.L.; Paul, M.J.; Taulbee, W.K. Stream ecosystem function in urbanizing landscapes. J. N. Am. Benthol. Soc. 2005, 24, 602–612. [Google Scholar] [CrossRef]
  5. Allan, J.D. Landscapes and riverscapes: The influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 2004, 35, 257–284. [Google Scholar] [CrossRef]
  6. Liao, H.; Sarver, E.; Krometis, L.H. Interactive effects of water quality, physical habitat, and watershed anthropogenic activities on stream ecosystem health. Water Res. 2018, 130, 69–78. [Google Scholar] [CrossRef]
  7. Power, M.E.; Stout, R.J.; Cushing, C.E.; Harper, P.P.; Hauer, F.R.; Matthews, W.J.; Moyle, P.B.; Statzner, B. Biotic and abiotic controls in river and stream communities. J. N. Am. Benthol. Soc. 1988, 7, 456–479. [Google Scholar] [CrossRef]
  8. Lee, J.-W.; Lee, S.-W.; Hwang, S.-J.; Jang, M.-H.; Won, D.-H.; An, K.-J.; Park, H.-J.; Lee, J. Establishing diagnosis systems for impaired stream ecosystem using stream/river ecosystem survey and health Assessment. Korean J. Ecol. Environ. 2020, 53, 1–10. [Google Scholar] [CrossRef]
  9. Feld, C.K.; Saeedghalati, M.; Hering, D. A framework to diagnose the causes of river ecosystem deterioration using biological symptoms. J. Appl. Ecol. 2020, 57, 2271–2284. [Google Scholar] [CrossRef]
  10. Verdonschot, P.F.M.; Verdonschot, R.C.M. The role of stream restoration in enhancing ecosystem services. Hydrobiologia 2023, 850, 2537–2562. [Google Scholar] [CrossRef]
  11. Assireu, A.; Sperber, C.; Alcântara, E.; Fernandes, G.W.; Hughes, R. Editorial: River and watershed restoration, rehabilitation, and conservation: Challenges, actions, and perspectives. Front. Environ. Sci. 2022, 10, 1032628. [Google Scholar] [CrossRef]
  12. Kim, S.; Lee, S.-W.; Park, S.-R.; Shin, Y.; An, K. Socioeconomic risks and their impacts on ecological river health in South Korea: An application of the analytic hierarchy process. Sustainability 2021, 13, 6287. [Google Scholar] [CrossRef]
  13. Lee, J.-W.; Lee, S.-W.; An, K.-J.; Hwang, S.-J.; Kim, N.-Y. An estimated structural equation model to assess the effects of land use on water quality and benthic macroinvertebrates in streams of the Nam-Han River System, South Korea. Int. J. Environ. Res. Public Health 2020, 17, 2116. [Google Scholar] [CrossRef] [PubMed]
  14. An, C.-H.; Han, J.-S.; Choi, J.-K.; Lee, H.-G. The effect of water conveyance of Boryeong Dam on structural changes of benthic macroinvertebrates community. Korean J. Environ. Ecol. 2018, 32, 381–391. [Google Scholar] [CrossRef]
  15. Norton, S.B.; Cormier, S.M.; Suter, G.W.; Schofield, K.; Yuan, L.; Shaw-Allen, P.; Ziegler, C.R. Caddis: The causal analysis/diagnosis decision information system. In Decision Support Systems for Risk-Based Management of Contaminated Sites; Springer: Berlin/Heidelberg, Germany, 2008; pp. 1–24. [Google Scholar]
  16. USEPA. Causal Analysis/Diagnosis Decision Information System (CADDIS); USEPA; Environmental Protection Agency Office of Research and Development: Washington, DC, USA, 2010. Available online: https://www.epa.gov/caddis (accessed on 26 February 2025).
  17. Nichols, S.; Webb, A.; Norris, R.; Stewardson, M. Eco Evidence Analysis Methods Manual: A Systematic Approach to Evaluate Causality in Environmental Science; eWater Cooperative Research Centre: Bruce, Australia, 2011. [Google Scholar]
  18. Ministry of the Environment (MOE). Guide to Diagnosis of Health Deterioratin in River Auatic Ecosystem; Ministry of the Environment Water Environment Policy Division: Sejong, Republic of Korea, 2019.
  19. Karr, J.R. Defining and measuring river health. Freshw. Biol. 1999, 41, 221–234. [Google Scholar] [CrossRef]
  20. Kelly, M.G.; Whitton, B.A. The trophic diatom index: A new index for monitoring eutrophication in rivers. J. Appl. Phycol. 1995, 7, 433–444. [Google Scholar] [CrossRef]
  21. Rai, R.; Sharma, S.; Gurung, D.B.; Sitaula, B.K.; Shah, R.D.T. Assessing the impacts of vehicle wash wastewater on surface water quality through physico-chemical and benthic macroinvertebrates analyses. Water Sci. 2020, 34, 39–49. [Google Scholar] [CrossRef]
  22. Laini, A.; Viaroli, P.; Bolpagni, R.; Cancellario, T.; Racchetti, E.; Guareschi, S. Taxonomic and functional responses of benthic macroinvertebrate communities to hydrological and water quality variations in a heavily regulated river. Water 2019, 11, 1478. [Google Scholar] [CrossRef]
  23. Von Bertrab, M.G.; Krein, A.; Stendera, S.; Thielen, F.; Hering, D. Is fine sediment deposition a main driver for the composition of benthic macroinvertebrate assemblages? Ecol. Indic. 2013, 24, 589–598. [Google Scholar] [CrossRef]
  24. Krynak, E.M.; Yates, A.G. Benthic invertebrate taxonomic and trait associations with land use in an intensively managed watershed: Implications for indicator identification. Ecol. Indic. 2018, 93, 1050–1059. [Google Scholar] [CrossRef]
  25. Musonge, P.S.L.; Boets, P.; Lock, K.; Damanik Ambarita, N.M.D.; Forio, M.A.E.; Verschuren, D.; Goethals, P.L.M. Baseline assessment of benthic macroinvertebrate community structure and ecological water quality in Rwenzori rivers (Albertine rift valley, Uganda) using biotic-index tools. Limnologica 2019, 75, 1–10. [Google Scholar] [CrossRef]
  26. Mwaijengo, G.N.; Vanschoenwinkel, B.; Dube, T.; Njau, K.N.; Brendonck, L. Seasonal variation in benthic macroinvertebrate assemblages and water quality in an Afrotropical river catchment, northeastern Tanzania. Limnologica 2020, 82, 125780. [Google Scholar] [CrossRef]
  27. Esri. ArcGIS Desktop: Release 10.6.1; Environmental Systems Research Institute: Redlands, CA, USA, 2018. [Google Scholar]
  28. Maruyama, G. Basics of Structural Equation Modeling; Sage: Washington, DC, USA, 1997; ISBN 0803974094. [Google Scholar]
  29. Bowen, N.K.; Guo, S. Structural Equation Modeling; Oxford University Press: Oxford, UK, 2011; ISBN 0199710104. [Google Scholar]
  30. Fernandes, A.C.P.; Sanches Fernandes, L.F.S.; Moura, J.P.; Cortes, R.M.V.; Pacheco, F.A.L. A structural equation model to predict macroinvertebrate-based ecological status in catchments influenced by anthropogenic pressures. Sci. Total Environ. 2019, 681, 242–257. [Google Scholar] [CrossRef] [PubMed]
  31. Li, X.; Zhang, Y.; Guo, F.; Gao, X.; Wang, Y. Predicting the effect of land use and climate change on stream macroinvertebrates based on the linkage between structural equation modeling and bayesian network. Ecol. Indic. 2018, 85, 820–831. [Google Scholar] [CrossRef]
  32. Bizzi, S.; Surridge, B.W.J.; Lerner, D.N. Structural equation modelling: A novel statistical framework for exploring the spatial distribution of benthic macroinvertebrates in riverine ecosystems. River Res. Appl. 2013, 29, 743–759. [Google Scholar] [CrossRef]
  33. Taniwaki, R.H.; Cassiano, C.C.; Filoso, S.; Ferraz, S.F.B.; Camargo, P.B.; Martinelli, L.A. Impacts of converting low-intensity pastureland to high-intensity bioenergy cropland on the water quality of tropical streams in Brazil. Sci. Total Environ. 2017, 584–585, 339–347. [Google Scholar] [CrossRef] [PubMed]
  34. Grimstead, J.P.; Krynak, E.M.; Yates, A.G. Scale-Specific Land Cover Thresholds for Conservation of Stream Invertebrate Communities in Agricultural Landscapes. Landsc. Ecol. 2018, 33, 2239–2252. [Google Scholar] [CrossRef]
  35. Tong, S.T.Y.; Chen, W. Modeling the relationship between land use and surface water quality. J. Environ. Manag. 2002, 66, 377–393. [Google Scholar] [CrossRef]
  36. Carlson, P.E.; Johnson, R.K.; McKie, B.G. Optimizing stream bioassessment: Habitat, season, and the impacts of land use on benthic macroinvertebrates. Hydrobiologia 2013, 704, 363–373. [Google Scholar] [CrossRef]
  37. Fergus, C.E.; Brooks, J.R.; Kaufmann, P.R.; Herlihy, A.T.; Hill, R.A.; Mitchell, R.M.; Ringold, P. Disentangling natural and anthropogenic effects on benthic macroinvertebrate assemblages in western US streams. Ecosphere 2023, 14, e4688. [Google Scholar] [CrossRef]
  38. Wagenhoff, A.; Townsend, C.R.; Phillips, N.; Matthaei, C.D. Subsidy-stress and multiple-stressor effects along gradients of deposited fine sediment and dissolved nutrients in a regional set of streams and rivers. Freshw. Biol. 2011, 56, 1916–1936. [Google Scholar] [CrossRef]
  39. Jowett, I.G. Hydraulic constraints on habitat suitability for benthic invertebrates in gravel-bed rivers. River Res. Appl. 2003, 19, 495–507. [Google Scholar] [CrossRef]
  40. Lee, S.H.; Lee, M.J.; Seo, E.W.; Lee, J.E. Analysis of functional habitat groups of benthic macroinvertebrates according to changes in the riverbed. J. Environ. Sci. Int. 2017, 26, 373–380. [Google Scholar] [CrossRef]
  41. Townsend, C.R.; Scarsbrook, M.R.; Dolédec, S. Quantifying disturbance in streams: Alternative measures of disturbance in relation to macroinvertebrate species traits and species richness. J. N. Am. Benthol. Soc. 1997, 16, 531–544. [Google Scholar] [CrossRef]
  42. Kim, J.; Kim, A.R.; Kong, D. Classification of microhabitats based on habitat orientation groups of benthic macroinvertebrate communities. J. Korean Soc. Water Environ. 2017, 33, 728–735. [Google Scholar]
  43. Ministry of the Environment (MOE). Diagnosis for Impaired Stream Ecosystem; Ministry of the Environment Water Environment Policy Division: Sejong, Republic of Korea, 2022.
  44. Gerth, W.J.; Li, J.; Giannico, G.R. Agricultural land use and macroinvertebrate assemblages in lowland temporary streams of the Willamette Valley, Oregon, USA. Agric. Ecosyst. Environ. 2017, 236, 154–165. [Google Scholar] [CrossRef]
  45. Ruangpan, L.; Vojinovic, Z.; Di Sabatino, S.; Leo, L.S.; Capobianco, V.; Oen, A.M.P.; McClain, M.E.; Lopez-Gunn, E. Nature-based solutions for hydro-meteorological risk reduction: A state-of-the-art review of the research area. Nat. Hazards Earth Syst. Sci. 2020, 20, 243–270. [Google Scholar] [CrossRef]
  46. Eisenber, B.; Chiesa, C.; Flascher, L.K.; Jakstis, K.; Polcher, V.; Schwarz-v.Raumer, H.-G. Nature-Based Solutions Technical Handbook Factsheets; UNaLab URBAN NATURE LABS Institut fur Landschaftsplanung und Okologi—ILPO: Santa Cruz de la Sierra, Bolivia, 2022. [Google Scholar]
  47. Korte, T. Current and substrate preferences of benthic invertebrates in the rivers of the Hindu Kush-Himalayan region as indicators of hydromorphological degradation. Hydrobiologia 2010, 651, 77–91. [Google Scholar] [CrossRef]
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Figure 1. Location of the study areas and impaired streams identified by the National Aquatic Ecosystem Assessment Program within the study areas.
Figure 1. Location of the study areas and impaired streams identified by the National Aquatic Ecosystem Assessment Program within the study areas.
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Figure 2. Structural equation model for describing causal relationships. Total nitrogen (TN); total phosphorus (TP); chemical oxygen demand (COD); biochemical oxygen demand (BOD); Ephemeroptera, Plecoptera, and Trichoptera (EPT); and benthic macroinvertebrate index (BMI).
Figure 2. Structural equation model for describing causal relationships. Total nitrogen (TN); total phosphorus (TP); chemical oxygen demand (COD); biochemical oxygen demand (BOD); Ephemeroptera, Plecoptera, and Trichoptera (EPT); and benthic macroinvertebrate index (BMI).
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Figure 3. Full model for benthic macroinvertebrate communities. Total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and benthic macroinvertebrate index (BMI).
Figure 3. Full model for benthic macroinvertebrate communities. Total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and benthic macroinvertebrate index (BMI).
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Figure 4. Optimized model for benthic macroinvertebrate communities. Total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and benthic macroinvertebrate index (BMI).
Figure 4. Optimized model for benthic macroinvertebrate communities. Total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and benthic macroinvertebrate index (BMI).
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Table 1. Descriptive statistics of the watershed/stream attributes and benthic macroinvertebrate community indicators of streams in the Han River Basin.
Table 1. Descriptive statistics of the watershed/stream attributes and benthic macroinvertebrate community indicators of streams in the Han River Basin.
ClassificationVariablesMeanS.D.Min.Max.
Benthic Invertebrate communitiesTotal number of benthic invertebrate communities13.066.383.0039.00
Density ratio of EPT taxa (%)34.0722.320.0086.76
Density ratio of tolerant taxa (%)57.6525.190.0099.66
Density ratio of collector–gatherer taxa (%)73.1218.864.68100.00
BMI grade3.331.261.005.00
Hydrological CharacteristicsFlow volume (m3/min)0.120.200.002.28
Flow velocity (cm/s)5.111.750.0310.00
Water QualityBOD (mg/L)2.231.390.4010.90
COD (mg/L)4.542.821.2026.30
TN (mg/L)3.662.190.1712.90
TP (mg/L)0.090.080.010.50
Turbidity (NTU)26.0963.820.00520.00
Stream Substrate CompositionSubstrate embeddedness (%)50.3327.9510.0090.00
Silty sand (%)46.9331.340.00100.00
Gravel (%)42.3427.160.0095.00
Bedrock (%)8.5614.640.0095.00
Land Use
Proportion		Urban area (%)11.8513.030.0061.50
Agricultural area (%)22.2620.440.0389.49
n = 331; S.D. = standard deviation; Min. = minimum; Max. = maximum; TN = total nitrogen; TP = total phosphorus; COD = chemical oxygen demand; BOD = biochemical oxygen demand; EPT = Ephemeroptera, Plecoptera, and Trichoptera; and BMI = benthic macroinvertebrate index.
Table 2. Standardized regression weights of the full model for benthic macroinvertebrate communities.
Table 2. Standardized regression weights of the full model for benthic macroinvertebrate communities.
PathBetaS.E.C.R.
Stream substrate composition→Water quality0.280.020.495
Stream substrate composition→Total number of benthic invertebrate communities−0.51 *0.07−2.122
Stream substrate composition→Density ratio of ETP taxa−1.61 **0.54−3.069
Stream substrate composition→Density ratio of collector–gatherer taxa1.16 *0.352.841
Stream substrate composition→Density ratio of tolerant taxa1.67 **0.633.042
Hydrological characteristics→Stream substrate composition0.40 ***2.593.436
Hydrological characteristics→Water quality−0.240.18−0.859
Hydrological characteristics→Total number of benthic invertebrate communities0.61 **1.223.253
Hydrological characteristics→Density ratio of ETP taxa0.96 **7.512.907
Hydrological characteristics→Density ratio of collector–gatherer taxa−0.86 **5.21−3.189
Hydrological characteristics→Density ratio of tolerant taxa−0.71 *8.45−2.162
Water quality→Total number of benthic invertebrate communities−0.25 *1.04−2.35
Water quality→Density ratio of ETP taxa−0.067.04−0.293
Water quality→Density ratio of collector–gatherer taxa0.134.610.839
Water quality→Density ratio of tolerant taxa−0.028.09−0.105
Land use proportion→Stream substrate composition−0.89 **1.81−3.591
Land use proportion→Hydrological characteristics0.120.050.735
Land use proportion→Water quality0.660.141.045
Land use proportion→Total number of benthic invertebrate communities−0.73 **0.68−2.296
Land use proportion→Density ratio of ETP taxa−2.01 *5.94−2.538
Land use proportion→Density ratio of collector–gatherer taxa1.44 *3.782.43
Land use proportion→Density ratio of tolerant taxa2.09 *7.282.434
Total number of benthic invertebrate communities→BMI grade0.34 **0.137.723
Density ratio of ETP taxa→BMI grade0.26 **0.054.357
Density ratio of collector-gatherer taxa→BMI grade−0.23 **0.05−4.701
Density ratio of tolerant taxa→BMI grade−0.13 *0.04−2.377
* p < 0.05. ** p < 0.01. *** p < 0.001. S.E. (Standard Error), C.R. (Critical Ratio).
Table 3. Standardized regression weights of optimized model for benthic macroinvertebrate communities.
Table 3. Standardized regression weights of optimized model for benthic macroinvertebrate communities.
PathBetaS.E.C.R.
Stream substrate composition→Total number of benthic invertebrate communities−0.37 **0.16−2.174
Stream substrate composition→Density ratio of ETP taxa−1.16 **1.05−3.733
Stream substrate composition→Density ratio of collector–gatherer taxa0.76 **0.663.287
Stream substrate composition→Density ratio of tolerant taxa1.07 **1.153.502
Hydrological characteristics→Stream substrate composition0.33 **0.473.483
Hydrological characteristics→Total number of benthic invertebrate communities0.34 **0.572.831
Hydrological characteristics→Density ratio of ETP taxa0.65 **3.083.532
Hydrological characteristics→Density ratio of collector–gatherer taxa−0.54 **2.25−3.396
Hydrological characteristics→Density ratio of tolerant taxa−0.41 **2.93−2.665
Water quality→Total number of benthic invertebrate communities−0.19 **0.27−3.423
Water quality→Density ratio of collector–gatherer taxa0.13 **0.722.637
Land use proportion→Water quality0.47 **0.053.839
Land use proportion→Stream substrate composition−0.83 **0.47−3.679
Land use proportion→Total number of benthic invertebrate communities−0.72 **0.48−3.001
Land use proportion→Density ratio of ETP taxa−1.64 **3.39−3.406
Land use proportion→Density ratio of collector–gatherer taxa1.10 **1.993.275
Land use proportion→Density ratio of tolerant taxa1.55 **3.663.36
Total number of benthic invertebrate communities→BMI grade0.34 **0.137.723
Density ratio of ETP taxa→BMI grade0.26 **0.054.357
Density ratio of collector-gatherer taxa→BMI grade−0.23 **0.05−4.701
Density ratio of tolerant taxa→BMI grade−0.13 *0.04−2.377
* p < 0.05. ** p < 0.01. S.E. (Standard Error), C.R. (Critical Ratio).
Table 4. A summary of the model fit indices. All indices indicate the high suitability of the estimated model to explain the data in this study.
Table 4. A summary of the model fit indices. All indices indicate the high suitability of the estimated model to explain the data in this study.
Model Fit IndexCriteriaFull ModelOptimized Model
NFI≥0.900.790.90
PNFI≥0.600.590.66
TLI≥0.900.750.90
CFI≥0.900.810.91
Normed fit index (NFI), Tucker–Lewis index (TLI), comparative fit index (CFI), and parsimony normed fit index (PNFI).
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Park, S.-R.; Park, Y.; Lee, J.-W.; Kim, H.; You, K.-A.; Lee, S.-W. How Land Use and Hydrological Characteristics Impact Stream Conditions in Impaired Ecosystems. Land 2025, 14, 829. https://doi.org/10.3390/land14040829

AMA Style

Park S-R, Park Y, Lee J-W, Kim H, You K-A, Lee S-W. How Land Use and Hydrological Characteristics Impact Stream Conditions in Impaired Ecosystems. Land. 2025; 14(4):829. https://doi.org/10.3390/land14040829

Chicago/Turabian Style

Park, Se-Rin, Yujin Park, Jong-Won Lee, Hyunji Kim, Kyung-A You, and Sang-Woo Lee. 2025. "How Land Use and Hydrological Characteristics Impact Stream Conditions in Impaired Ecosystems" Land 14, no. 4: 829. https://doi.org/10.3390/land14040829

APA Style

Park, S.-R., Park, Y., Lee, J.-W., Kim, H., You, K.-A., & Lee, S.-W. (2025). How Land Use and Hydrological Characteristics Impact Stream Conditions in Impaired Ecosystems. Land, 14(4), 829. https://doi.org/10.3390/land14040829

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