Impact of Land Use Change on Seasonal Water Quality, Case Study in Chi-Mun River Basin in Thailand

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It is the first of its kind in the region and the Mekong River Basin, providing critical insights for global river basin management. The research analyzes spatial and temporal land use changes and their multi-scale impacts on water quality, utilizing land use change estimation, water quality index analysis, and redundancy analysis (RDA). The results showed that stream water quality variables displayed highly temporal variations, with pH, Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal Coliform Bacteria (FCB), Total Phosphorus (TP), Nitrate Nitrogen (NO 3 -N), Ammonia-nitrogen (NH 3 -N), Suspended Solids (SS) all generally displaying higher levels in the wet season, while there were higher concentrations of Dissolved Oxygen (DO), Electrical Conductivity (EC), and Water Quality Index (WQI) in the dry season. The water samples were collected once in January, March, May, and August from 2007 to 2024. The water quality in wet season is represented in May and August, while in dry season is represented in January and March. The total contribution of land use patterns on overall water quality was stronger during the wet season. It shows a decline in paddy and forest areas alongside an expansion of urban, agricultural, and aqua agricultural land. Water quality displayed significant seasonal variations, with forests and water bodies contributing to purification, while agricultural and urban areas degraded water quality. The findings offer recommendations for water quality protection and land management policies that align with the basin’s natural and socio-economic characteristics, promoting coordinated regional development. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Land Use Change Water Quality Seasonal Variations Anthropogenic Impact Redundancy Analysis (RDA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Both natural and human-induced factors contribute to changes in water quality, with human activities having a more visible effect. Intensive human activities can affect land use patterns, thereby influencing water quality (Tong et al., 2023 ; Wang et al., 2024 ). The quality of water plays an essential role in protecting habitats, supporting agriculture, sustaining industry, and safeguarding public health (Akasaka et al., 2010 ; Lu et al., 2015). It is challenging to maintain water quality due to the presence of both point source (PS) and non-point source (NPS) pollution. PS pollution primarily includes industrial and domestic wastewater loads, which can be relatively easily identified (Wang et al., 2016 ). On the other hand, NPS typically comes from diffuse sources like agriculture, runoff, and the deposition of atmospheric pollutants (Ongley et al., 2010 ). Identifying NPS pollution is complicated by the complex interplay of rainfall and landscape characteristics (Liu et al., 2016 ). As the social economy, industry, agriculture, and urbanization continue to progress, water quality will inevitably decline (Yang et al., 2023). The response of the surface water environment to changes in land use patterns has attracted considerable scholarly attention in recent years (Peng et al., 2020 ; Xue et al., 2022 ). Land use changes have indirect effects on water quality by changing land surface characteristics, affecting regional hydrological cycles, and biochemical cycles. For example, changes in land use led to variations in land cover types and the intensity of land use, which affect the soil's capacity to retain water and soil erosion. These changes impact processes such as evapotranspiration, runoff formation, and groundwater recharge, leading to the accumulation of pollutants (Arcega-Cabrera et al., 2021 ). Urbanization, for instance, increases surface runoff, reduces evaporation and infiltration, and increases the accumulation of pollutants (Schulte-Uebbing et al., 2022 ). Therefore, the impact of land use types on water quality exhibits marked variations across different spatiotemporal scales (Crites et al., 2021 ). The variations in patterns of land use in temporal and spatial, including the characteristics at different scales generate uncertainty in understanding how different types of land use affect the quality of water (Ding et al., 2016 ). As a result, researchers mainly investigate understanding how water quality responds at different scales, with most existing studies concentrating on sub-basin and riparian scales (Wang et al., 2023a; Zhang et al., 2019 ). Several studies have shown that the types of land use within riparian buffers have a more significant impact on water quality than those at the sub-basin level. However, the previous studies unidentified the specific scale at which riparian buffers most significantly affect water quality, possibly due to differences in location and research approaches (Han et al., 2023 ; Mainali and Chang, 2018 ; Xu et al., 2020 ). Therefore, this study examines various spatial scales of sub-basin for analysis. Additionally, significant differences in water quality between dry and wet seasons within the same watershed have been observed (Liu et al., 2021 ; Shi et al., 2017 ). The high-intensity rainfall during the wet season can have a considerable impact on water quality in the watershed. This has led to increased research focus on the relationship between seasonal changes in land use and water quality in the river basin (Michalak, 2016 ; Shi et al., 2025 ). Due to the complex nature and the effects of changes in land use, the way water quality responds to seasonal variations in types of land use continues to be a topic that requires further investigation. At the scale of the study area, most existing studies have pointed from small to medium basins. For instance, research conducted in 1995 on the Bentong River in Malaysia revealed that forests possess strong water purification capabilities (Shu et al., 2022 ). Wei et al. ( 2020 ) investigated the water quality of different lakes and reservoirs in China by considering land use types, while Zhou et al. ( 2022 ) inspected the correlation between land use and water quality in the Shiyang River Basin on the northern slope of the Qilian Mountains. However, there is relatively less research focusing on large-scale basins and comparative studies between basins. Large-scale basins encompass extensive areas, including multiple regions and cities, as well as diverse landforms and ecosystems. It is crucial to understand the impact of land use patterns on water quality in large-scale basins for water resources management, ecological conservation, and regional development planning. Moreover, given the ongoing societal progress, regional coordinated development is increasingly critical, surpassing the adequacy of single-basin studies. Comparative studies between basins can not only uncover shared geographical processes and rules but also deliver water quality response outcomes tailored to the distinctive characteristics of each basin by integrating their unique natural and social environments (Zhou et al., 2021 ). These studies can advance water environmental protection and sustainable development in diverse basins, and offer guidance in formulating cross-regional water quality and land management policies (Ke and Zhang, 2024 ; Ouyang et al., 2018 ). The Chi-Mun River Basin in Thailand, which is the largest tributary of the Mekong River Basin, covers a wide area and has diverse natural and socio-economic conditions, leading to varied land use patterns. Moreover, as a result of the area's distinct monsoon and continental climate, including the impact of typhoons, significant seasonal and spatial differences in rainfall are evident in major river basins in Thailand. The Chi-Mun River Basin in northeastern Thailand is heavily influenced by the subtropical monsoon climate, receiving abundant annual precipitation mainly during the rainy season (April–October). Basins in the northeast are primarily impacted by temperate monsoon and continental climates, with higher annual precipitation averaging around 900–1,500 mm (Kite, 2001 ; Serbpongpan, 2004 ; Artlert et al., 2013; Li et al., 2020 ; Pawar et al., 2023 ). This research focused on the Chi-Mun River Basin in Thailand (as shown in Fig. 1 ) and investigated a correlation between changes in land use and water quality on a large river basin scale. This is the first study to investigate the correlation between land use change and water quality parameters for the river basin in Thailand and the Mekong River Basin. The study holds importance in achieving harmonized development across different regions and offers insights for comparative studies on large-scale river basins globally. Thus, the objective of this study is to (1) characterize the spatial and temporal variations of land use change patterns and water quality parameters in the Chi-Mun River basins in Thailand; (2) quantify the correlation between land use change patterns and water quality in the basin; (3) investigate the multi-scale impacts of land use change patterns of basins on water quality parameters. 2. Materials and methods The method, presented in Fig. 2 , aims to address the research question of this study. It is based on three components: land use change estimation, water quality index analysis in seasonal, and relationship analysis between land use change and water quality index. 2.1 Study area The Chi-Mun River Basin, which is the largest tributary of the Mekong River Basin, is situated in the northeastern region of Thailand within the coordinates 14°-16° N, 101°-106°E. This expansive basin covers an area of approximately 120,000 square kilometers, as illustrated in Fig. 1 . It contributes around 25,000 million cubic meters (MCM) of flow to the Mekong River through an annual runoff of approximately 800 cubic meters per second (m3/s). The majority of this runoff takes place during the rainy season, which spans from April to October, while the dry season is characterized by lower flow rates. The rainfall in the river basin is predominantly influenced by the monsoon during the rainy season, with an annual precipitation range of approximately 900 to 1,500 millimeters. The topography of the river basin features mountains along the border and flat terrain in the central area, with elevations ranging from 200 to 2,000 meters. The natural vegetation in the basin primarily consists of forests and shrubs. 2.2 Land use data The land use change pattern in the Chi-Mun River Basin was estimated by the data that was provided by the Land Development Department ( http://www1.ldd.go.th/ ), Thailand. The data was collected from 2007 to 2021 and includes eight land use types: paddy fields, agriculture, farms, aquacultural agriculture, miscellaneous, urban areas, and water (as shown in Fig. 3 ). We used the Quantum Geographic Information System (QGIS 3.34.7-Prizren) to evaluate the importance of the spatial extent of the effects of land use on water quality in the river basin. Paddy revealed the main land use type in the study area, followed by Agriculture, and Forest (as shown in Table 1 ). Table 1 Land use types from 2007 to 2021. Year Area each land use type, km2 Paddy Agr. Farm Aqua. Forest Misc. Urban Water 2007 58,387 24,292 337 109 19,475 4,859 6,262 3,653 2008 58,542 23,797 525 120 19,462 5,110 6,216 3,594 2009 58,542 23,797 525 120 19,462 5,110 6,216 3,594 2010 56,298 25,905 553 132 18,644 5,452 6,519 3,871 2011 56,298 25,905 553 132 18,644 5,452 6,519 3,871 2012 56,298 25,905 553 132 18,644 5,452 6,519 3,871 2013 56,298 25,905 553 132 18,644 5,452 6,519 3,871 2014 55,461 26,833 510 141 18,431 5,211 6,890 3,897 2015 54,625 27,761 467 150 18,218 4,970 7,262 3,923 2016 54,630 27,841 468 148 18,143 4,959 7,262 3,924 2017 53,039 31,161 390 156 17,300 4,067 7,124 4,139 2018 52,434 31,161 390 156 17,300 4,671 7,124 4,139 2019 50,701 32,679 410 182 16,961 4,518 7,558 4,364 2020 50,701 32,679 410 182 16,961 4,518 7,558 4,364 2021 50,701 32,679 410 182 16,961 4,518 7,558 4,364 2.3 Water quality data Water quality data was monitored and provided by the Ministry of Natural Resources and Environment ( https://rwater.mnre.go.th/ ) and the eleven sampling stations in the Chi-Mun River are shown in Fig. 1 c. The three stations, CI01, CI02, and CI03, are in the Chi River, and the eight stations, MU01, MU02, MU03, MU04, MU05, MU06, MU07, and MU08, are in the Mun River. Water samples were collected once in January, March, May, and August from the year of 2007 to 2024. The time series data of the water quality, as shown in Fig. 4 , were split into two seasons, wet and dry. The data for the wet season is in the May and August, and the dry season is in the January and March. To analyze the environmental quality of the surface water presented by eleven parameters: pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal Coliform Bacteria (FCB), Total Phosphorus (TP), Nitrate Nitrogen (NO 3 -N), Ammonia-nitrogen (NH 3 -N), Suspended Solids (SS), Electrical Conductivity (EC), and Water Quality Index (WQI). The pH average presented approximately 7.3 considering all stations; the pH peak was in 2009. The DO had an average of approximately 3.3 mg/L, the peak of DO was in the wet season of 2007. The BOD was averaged by approximately 1.3 mg/L with a peak in 2015 and 2019. The TCB was 7,000 MPN/100ml as the average value with peaks in 2009, 2010, 2015, and 2017. The FCB had an average value of approximately 0.07 mg/L with a peak at the MU04 station in 2017. The TP was approximately 0.8 of the average value and the peak was approximately 0.1 mg/L at the MU08 station. The NO 3 -N was represented with 0.7 for the average value with the peak in 2013 and 2015 at the CI01, CI02, and CI03 stations. The NH 3 -N was average at 0.35 mg/L with two peaks in 2013 and 2017. The SS presented an average value of 21 mg/L with a peak in 2007 and 2014. The EC was 230 µS/cm with a peak in 2021. The WQI was represented by 70 for the average value for all stations. 2.4 Statistical analysis The water quality data's normality was assessed using the Kolmogorov-Smirnov test. The independent samples t-test was employed to verify seasonal variations in water quality parameter concentrations to compare two independent samples for significant differences. Spatial distinctions among water quality parameters across monitoring stations were examined using one-way analysis of variance (ANOVA) to identify significant differences among multiple independent samples. Redundancy analysis (RDA) is frequently utilized to examine the connections between species variables and environmental variables (Kleyer et al., 2012 ). We utilized the R platform's RDA library to investigate changes in water quality and land use types across various spatial scales within the river basin (as shown in Fig. 2 ). In the RDA analysis plot, two distinct types of arrows symbolize response variables and explanatory variables, respectively. If the angle between the arrows of two variables is acute, they are positively correlated; if the angle is obtuse, they are negatively correlated. Moreover, the angle between the arrows changes inversely with the degree of correlation between the two variables. The impact of each variable on water quality is directly proportional to the length of the arrows. Furthermore, RDA analysis evaluates how much each type of land use explains changes in all water quality parameters (Uwacu et al., 2021). 3. Results The results aim to answer the research questions based on the method of this study, which was divided into three issues. First, the land use change was used to present the percentage change in land use in the study area during the period from 2007 to 2021. Next, an independent samples t-test was used to present the temporal changes in water quality, demonstrating significant disparities between seasons and offering a detailed illustration of the spatial and temporal changes in water quality. Finally, redundancy analysis (RDA) was used to demonstrate the impact of land use on the change in water quality. 3.1 Land use change The land use in the Chi-Mun River Basin was analyzed from 2007 to 2021. The percentage of each land use type and the changes over time are shown in Fig. 5 . During this period, there was a decrease in the area of paddy fields, farms, forests, and miscellaneous land use. In contrast, there was an increase in agriculture, aqua agriculture, urban areas, and water. In 2007, paddy fields covered approximately 50% of the total area, decreasing to approximately 43% by 2021 with a decreasing rate of approximately 0.4 percent/year, as shown in Fig. 5 a1. Figure 5a2 shows the most significant decrease in paddy fields between 2009 and 2010. Agriculture, the second most dominant land use type, increased from approximately 20% in 2007 to approximately 27.5% in 2021 (see Fig. 5 b1), with an increasing rate of approximately 0.5 percent/year and the highest increase observed in 2016 (as shown in Fig. 5 b2), at approximately 3%. The farm area increased from approximately 0.3% in 2007 to approximately 0.45% in 2009 and decreased to approximately 0.3% in 2021 as presented in Fig. 5 c1, with the most significant approximately 0.16% increase in 2008 and a decrease of approximately 0.06% in 2017 (see Fig. 5 c2). The aqua agriculture area increased from approximately 0.1% in 2007 to approximately 0.15% in 2021 as presented in Fig. 5 d1, with the most significant decrease between 2019 and 2020 (see Fig. 5 d2). The forest area decreased from approximately 16.5% in 2007 to about 14.5% in 2021 as presented in Fig. 5 e1, with a decreasing rate of approximately 0.15 percent/year and the most significant decrease between 2016 and 2017 (see Fig. 5 e2). The miscellaneous area increased from approximately 4.0% in 2007 to approximately 4.5% in 2011 and decreased to approximately 4.0% in 2021 as presented in Fig. 5 f1, with the most significant approximately 0.7% decrease in 2017 and an increase of approximately 0.5% in 2018 (see Fig. 5 f2). Urban areas increased from approximately 5.25% in 2007 to approximately 6.5% in 2021, as presented in Fig. 5 g1, with an increasing rate of approximately 0.1 percent/year and the highest increase observed between 2013 and 2014 at approximately 0.32%, as shown in Fig. 5 g2. Finally, water areas increased from approximately 3.25% in 2007 to approximately 3.75% in 2021, as presented in Fig. 5 h1, with the highest increase observed between 2009 and 2010 at approximately 0.24%, as shown in Fig. 5 g2. 3.2 Temporal variation of water quality The results of the independent samples t-test revealed that the levels of pH, BOD, SS, and WQI exhibited statistically significant differences between seasons, with a significance level of P < 0.05, as demonstrated in Fig. 6 . Furthermore, Fig. 7 provides a comprehensive depiction of the spatial and temporal fluctuations in water quality. Notably, during the dry season, elevated levels of pH, BOD, and NH 3 -N were observed, while DO, TCB, FCB, TP, NO 3 -N, SS, EC, and WQI exhibited higher values in the wet season. It is worth noting that the maximum concentrations of DO, NH 3 -N, and SS were exclusively observed during the wet season of 2007. Over the years from 2007 to 2024, a significant decrease in pH was noted during the dry season, while an increase was observed in the wet season. Additionally, there was a slight increase in the levels of DO, BOD, TCB, NO 3 -N, NH 3 -N, and EC during both the dry and wet seasons over the same period. Conversely, FCB, TP, SS, and WQI experienced a noteworthy decrease during the same timeframe for both dry and wet seasons. Figure 7 a presented pH values across dry, wet, and annual periods in an average for all stations that generally show stability with values approximately 7.0 to 8.0. The wet season tends to show slightly more variability in comparison to the dry season. The DO presents a gradual upward trend, especially noticeable in the annual and wet season data, with values increasing from around 6 mg/L in the earlier years to approximately 8 mg/L by 2022, as shown in Fig. 7 b. It found that the dry season data is more variable compared to the wet season. Figure 7 c reveals the BOD level remained relatively low, generally fluctuating between 0 and 4 mg/L across all three periods. There is some higher variability, particularly in the wet season and annual, with occasional spikes in certain years. TCB displayed considerable variation across the years, particularly in the wet season, as shown in Fig. 7 d. Significant peaks occur during certain years, with TCB values exceeding 20,000 MPN/100 mL, particularly around 2012–2014. The dry season tended to show lower median values compared to the wet season. FCB followed the pattern of the TCB, as presented in Fig. 7 e, with higher variability in the wet season and showed a peak between 2009 to 2014. A pattern of decay is in both dry and wet seasons after 2015, suggesting an improvement in water quality. TP revealed relatively low with most value of approximately 0.3 mg/L as shown in Fig. 7 f. Its trend showed a decay, especially in the wet season and annually indicating potential reductions in nutrient pollution over time. The highest variable and concentration levels were presented from 2007 to 2010. Figure 7 g presents the NO 3 -N concentration variability across seasons, with a peak occurring from 2010 to 2015, especially in the wet season and annually. The dry season trended to a low value of approximately 0.1 mg/L for occasional spikes in 2014. Overall, the levels of NO 3 -N decreased across all periods after 2016. NH 3 -N was relatively higher in 2015 and presented in the wet season and annually, as shown in Fig. 7 h. The NH 3 -N showed a significant reduction in concentrations for all seasons with a value of approximately 0.5 mg/L after 2016. The dry season showed values lower than the wet season to indicate seasonal impacts on the NH 3 -N level. Figure 7 i shows the SS concentrations, particularly during the wet season revealed a high peak in 2007 of approximately 250 mg/L. The SS levels decay after 2015 in stability at a lower level for both the dry and wet seasons. The annual data revealed the same trend, showing an overall decrease in SS after 2015. EC remained relatively stable over time with a value approximately from 150 to 350 µS/cm across all periods, as shown in Fig. 7 j. Slightly higher values were presented during the dry season compared to the wet season. It revealed that small peaks were visible in 2017 and 2021. The WQI value revealed a range from approximately 50 to 100 to suggest that water quality varies from medium to good. The wet season presented slightly more variability than the dry season. Overall, there is a slight improvement in the WQI to indicate a positive trend in the water quality after 2015 (see Fig. 7 k). However, the WQI in the wet season was generally lower than in the dry season to consider in the seasonal variation. 3.3 Effect of land use on water quality The redundancy analysis (RDA) method measured the influence of different land uses on water quality throughout different seasons. It identified Farm, Forest, and Urban as the land use types with the most significant impact on water quality during the dry season (Fig. 8 a). During the wet season, Paddy had the highest effect on water quality, followed by Farm and Urban (Fig. 8 b). For the annual period, Paddy, Farm, Forest, and Urban contributed the most to water quality changes (Fig. 8 c). The RDA results showed that the impact of land use types on water quality varied significantly depending on the season. Specifically, the response of water quality to land use was more pronounced during the dry season. Overall, the predictors explained approximately 90% of the variations in water quality across all land use types in the annual period. The specific land use types that had the most significant impact on water quality were identified based on their contribution rates. During the dry season, the Farm land use type had a substantial influence, accounting for 20.5% of the impact on water quality. On the other hand, during the wet season, the Paddy land use type greatly affected water quality, contributing to 30.2% of the impact. Together, these two land use types represented 21.9% of the annual impact on water quality. This is attributed to the increased precipitation during the wet season, which washes pollutants from the Paddy areas into nearby water bodies, leading to changes in water quality. These findings are visually represented in Figs. 8 and 9 , illustrating the varying impact of different land use types on water quality across different seasons. The RDA (Redundancy Analysis) plot provides a visual representation of the relationships between different land use types and water quality parameters. In Fig. 9 , the angles between arrows representing different variables reflect the varying correlations between land use types and water quality parameters. Figure 9 a corresponds to the dry season, while Fig. 9 b corresponds to the wet season. Figure 9 c is utilized to present the annual data. Across different spatial and temporal scales, Urban, Agriculture, Aqua agriculture, and Water demonstrate a negative association with pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total phosphorus (TP), ammonia nitrogen (NH 3 -N), nitrate nitrogen (NO 3 -N), suspended solids (SS), total coliform bacteria (TCB), and water quality index (WQI) during the dry season. Conversely, Paddy, Forest, Farm, and Miscellaneous exhibit a positive association with fecal coliform bacteria (FCB). Additionally, during the wet season, Paddy, Forest, Farm, and Miscellaneous show a negative association with FCB, SS, DO, and NH 3 -N. Furthermore, Urban, Agriculture, Aqua agriculture, and Water demonstrate a positive association with TCB, NO 3 -N, BOD, and TP. The annual correlation pattern exhibits a direction similar to the wet season. 4. Discussion This discussion explores the complex relationship between land use change and water quality. It looks at how factors such as agriculture, urbanization, and forestry can affect water ecosystems by introducing nutrients. Understanding these relationships is crucial for effective water resource management. The discussion also considers the impact of seasonal variations and long-term trends on water quality, providing important insights into the dynamics of river basins. Additionally, practical suggestions for river basin management are offered, emphasizing the need for integrated strategies that take into account both land use practices and temporal variability to protect water quality and maintain ecological balance. 4.1 Relationships between land use change and water quality This study examined the influence of various land use patterns on water quality across different spatial and temporal scales, as shown in Fig. 9 . Agriculture, Aqua agriculture, Urban, and Water were found to positively affect water quality, while Paddy, Farm, Forest, and Miscellaneous had a negative impact, consistent with previous research findings (Camara et al., 2019 ; Cole et al., 2020 ; Shi et al., 2019 ). The Redundancy Analysis (RDA) in Fig. 9 showed distinct relationships between land use types and water quality parameters during dry, wet, and annual periods. Urban and Agriculture (cropland) areas exhibited strong positive correlations with EC, WQI, and other pollutants, particularly in regions dominated by agriculture and urbanization in the Chi-Mun basin. The area was characterized by intensive farming and urban expansion, contributing significantly to nonpoint source pollution due to runoff containing excess fertilizers, pesticides, and organic waste from livestock. The urban area also introduced pollutants, including heavy metals and organic matter from industrial and domestic sources, into nearby water bodies (Liu et al., 2019 ; Miranda et al., 2022 ). Conversely, Forest, Miscellaneous, and Water, as a natural area, were associated with improved water quality. These ecosystems negatively correlated with BOD, DO, WQI, and NH 3 -N, indicating their role in filtering pollutants and reducing nutrient loads. If the concentration of inorganic nitrogen surpasses 0.3 mg/L in the spring, it indicates an adequate nitrogen level to facilitate the growth of summer algae blooms (UMassAmherst, 2016 ). Vegetation can mitigate soil erosion, intercept chemical runoff, and absorb nutrients, while Water enhanced water quality through hydrological regulation and nutrient transformation (Canet-Martí et al., 2022 ; Wang et al., 2021 ). Moreover, the natural filtration processes of aquifers and riverbeds help purify water, particularly during low precipitation periods, ensuring a balance in water quality and ecosystem health (Feng et al., 2023 ). 4.2 Effect of temporal scale on water quality The RDA results showed that there are variations in the correlation between land use patterns and water quality indicators across the Chi-Mun basins. In the dry season, land use has a more significant impact on water quality compared to the wet season. This is because the undulating terrain in the basins affects the flow rate of surface runoff, which in turn influences the rate of pollutant accumulation, absorption, and transformation, leading to different water quality results in the basins (Bai et al., 2023 ). For instance, in the middle and lower parts of the Chi-Mun River, where there are numerous plains and a dense river network, slow water flow in river channels during the dry season leads to the accumulation of pollutants, which negatively affects water quality. On the other hand, the highland areas upstream of the basin have loose soil that is easily eroded by rainwater, significantly affecting water quality in the wet season. Additionally, factors such as precipitation, river flow, and the timing of agricultural cultivation also play a role. Excessive and heavy precipitation can lead to changes in river flow, weakening the regulation of pollutants by wet season land use patterns (Karimi et al., 2023 ; Pinto et al., 2023 ; Wang et al., 2023). Crop growth and cultivation times vary due to differences in temperature and precipitation, and the use of large quantities of fertilizers and pesticides during the cultivation period results in a rapid increase in pollutant concentrations, leading to significant changes in water quality (Ahmad et al., 2021 ). Consequently, the contribution of the wet season differs from that of the dry season. 4.3 Suggestion to river basin management The Northern part of Thailand is experiencing ongoing urbanization, posing significant challenges to the ecological water environment in the Chi-Mun basin. It is crucial to develop effective land management policies and implement ecological protection measures to address these challenges. Vegetation and water play a vital role in improving water quality in the basin, highlighting the need to prioritize their protection in policy formulation. Measures such as returning farmland to forests and establishing vegetation protection zones are essential for enhancing ecological construction within the basin. Agriculture and urban activities have a negative impact on water quality in all basins, but this can be mitigated through careful planning, precise fertilizer application, and proper wastewater treatment before discharge. In addition to common characteristics, geographic and socio-economic factors also influence the environment in the basin. During the wet season, soil erosion, surface undulation, and certain land use types have negative impacts on water quality, necessitating better prevention of winds and sand, and reduction of agriculture and urban land use. It is essential to build and improve rainwater management systems to reduce direct urban or agricultural surface runoff into the river. During the dry season, safeguarding water quality is critical, requiring repairs and improvements to the substrate, enhancing the river's self-purification capacity, and reducing agricultural runoff through measures such as water transfer projects and construction of artificial wetlands, as well as adopting efficient water-saving irrigation techniques. 5. Conclusion The land use patterns in the Chi-Mun River basin of Thailand have changed in recent years. There has been a decrease in paddy and forest areas, while urban, water, Aqua agriculture, and agricultural land areas have expanded significantly in an increase. The shares of other land use types in the area have fluctuated. Water quality in the basin has shown significant seasonal variations between wet and dry seasons. All water quality indicators have displayed significance among the basins in different seasons, except for EC and WQI concentration in the basin during the dry season, which remained relatively stable. Forest, miscellaneous, and water have demonstrated water purification effects, whereas agriculture and urban areas have exacerbated water quality degradation. This research investigated the impacts and scale effects of land use on water quality across different time scales in the Chi-Mun basin of Thailand and put forward corresponding recommendations for water quality protection, considering the characteristics of the natural and socio-economic environments of the basin. It aids in the development of rational and effective land management policies and ecological protection measures tailored to the specific conditions of the basin, providing practical importance for the scientific management of water environments and regional coordinated development. Declarations CRediT authorship contribution statement Kwanchai Pakoksung: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. Nantawoot Inseeyong: Validation, Investigation . Nattawin Chawaloesphonsiya: Validation, Methodology, Investigation, Data curation, Writing – review & editing. Patiparn Punyapalakul: Validation, Methodology, Investigation, Supervision, Data curation, Writing – review & editing. Pichet Chaiwiwatworakul: Validation, Methodology, Investigation, Supervision, Data curation, Writing – review & editing. Mengzhen Xu: Validation, Methodology, Investigation, Supervision, Writing – review & editing. Pavisorn Chuenchum: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Investigation, Supervision, Resources, Funding acquisition, Formal analysis, Data curation, Conceptualization. Declaration of competing 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. Acknowledgement We would like to express our deepest gratitude to the Ministry of Natural Resources and Environment, Thailand, for providing the water quality data and the Land Development Department for the land use data essential to this study. We also extend our appreciation to the Department of Water Resources Engineering, Faculty of Engineering, Chulalongkorn University, and Tsinghua University for their technical support and insightful discussions throughout the research process. Finally, we acknowledge the invaluable feedback provided by our reviewers, whose suggestions greatly enhanced the quality of this manuscript. Funding This research is funded by Thailand Science Research and Innovation Fund, Chulalongkorn University (No. 6641/2566). Data availability The data that support the findings of this study are available from the Ministry of Natural Resources and Environment, Thailand, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of the Ministry of Natural Resources and Environment, Thailand. Ethical approval The authors undertake that this article has not been published in any other journal and that no plagiarism has occurred. Consent to participate The authors agree to participate in the journal. Consent to publish The authors agree to publish in the journal. References Tong, S., Li, W., Chen, J., Xia, R., Lin, J., Chen, Y., Xu, C.-Y., 2023. A novel framework to improve the consistency of water quality attribution from natural and anthropogenic factors. J. Environ. 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Ecological wetland paradigm drives water source improvement in the stream network of Yangtze River Delta. J. Environ. Sci. 110, 55–72. https://doi.org/10.1016/j.jes.2021.03.015. UMassAmherst, 2016. Fact Sheets, Massachusetts Water Watch Partnership. (assess: https://www.umass.edu/mwwp/resources/factsheets.html#:~:text=If%20these%20inorganic%20forms%20of,nitrogen%20can%20limit%20algae%20growth) Feng, Z., Xu, C., Zuo, Y., Luo, X., Wang, L., Chen, H., Xie, X., Yan, D., Liang, T., 2023. Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression. Environ. Res. 216, 114587 https://doi.org/10.1016/j.envres.2022.114587. Bai, Y., Zhao, Y., Huang, L., Shen, D., Sun, G., 2023. Numerical simulation of velocity distribution and pollution retention in flexible submerged vegetated channel. J. Hydrol. 626, 130265 https://doi.org/10.1016/j.jhydrol.2023.130265. 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Rep. 11 (1), 16526. https://doi.org/10.1038/s41598-021-96075-3. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5341317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382720797,"identity":"7a68a0a3-808b-414f-b096-c7459d854a9f","order_by":0,"name":"Kwanchai Pakoksung","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Kwanchai","middleName":"","lastName":"Pakoksung","suffix":""},{"id":382720798,"identity":"7dcf07e1-07ea-4077-b091-a2193b2f92e4","order_by":1,"name":"Nantawoot Inseeyong","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Nantawoot","middleName":"","lastName":"Inseeyong","suffix":""},{"id":382720799,"identity":"25a7832b-ad80-4bd7-b368-e8ec8540a764","order_by":2,"name":"Nattawin Chawaloesphonsiya","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Nattawin","middleName":"","lastName":"Chawaloesphonsiya","suffix":""},{"id":382720800,"identity":"c060726c-9e69-463a-8bf9-be17f948b3ff","order_by":3,"name":"Patiparn Punyapalakul","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Patiparn","middleName":"","lastName":"Punyapalakul","suffix":""},{"id":382720801,"identity":"9556fcfa-7f67-4523-9c8c-07d836f79551","order_by":4,"name":"Pichet Chaiwiwatworakul","email":"","orcid":"","institution":"King Mongkut’s University of Technology Thonburi","correspondingAuthor":false,"prefix":"","firstName":"Pichet","middleName":"","lastName":"Chaiwiwatworakul","suffix":""},{"id":382720802,"identity":"624a1b6b-08c1-45df-8d43-ac00bf2c889d","order_by":5,"name":"Mengzhen Xu","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Xu","suffix":""},{"id":382720803,"identity":"991e1cf0-3587-4099-9ced-f1febb218f06","order_by":6,"name":"Pavisorn Chuenchum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie2PsQrCMBRFnxR0Sen6wMEf6BBxcVD8lZRCJxFEEN0CQiahs98hiGNKB5fqJ4gidHIpXXQztigupo4OOdPlweHdC2Aw/CEDDiCL1IilBdAtzzX+XaHypZCAKQXLc1ShlKBLf1R2STu6zY4jQJLlY4Et1fBkZVvNluWQxiSZTADtdXMlsM1JQCFKNF8gYHFNMI+jvbFsgWr4UBUTGsVJWXQvFJI+lQF3rhUK+lLaheLWn4oKVV/wItUW5gkSdJrkgL7AlMq9tpi3yG4z5oWN+JyTaa8fOv75NNcob+qfQf4gGAwGg0HDA1fOT5bVAa2/AAAAAElFTkSuQmCC","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":true,"prefix":"","firstName":"Pavisorn","middleName":"","lastName":"Chuenchum","suffix":""}],"badges":[],"createdAt":"2024-10-27 12:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5341317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5341317/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71550864,"identity":"58cdf16e-c9be-49ed-bc0a-fc7be3750836","added_by":"auto","created_at":"2024-12-16 15:47:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":402506,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area, a) the location of the study area in Thailand, b) the detail of the study area as the Chi-Mun River basin, and c) the location of the observation points in the red box of the figure b. The blue line is the river network and the blue triangle is the observation point\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/ec9bb33b57fea43cc08e19db.png"},{"id":71550380,"identity":"8db43b3a-9aad-4bcc-8599-5a219c01fbe2","added_by":"auto","created_at":"2024-12-16 15:39:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62934,"visible":true,"origin":"","legend":"\u003cp\u003eThe methodology flow of this study used to determine the relationship between land use change and water quality\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/eb8ebc2d04b96eee1ffa4f13.png"},{"id":71550377,"identity":"c8c7e286-9489-449e-9c92-68236df4423c","added_by":"auto","created_at":"2024-12-16 15:39:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":892785,"visible":true,"origin":"","legend":"\u003cp\u003eLand use data from 2007 to 2021\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/d2ac97d57b762674ca944b4e.png"},{"id":71550364,"identity":"44cb0185-4853-4868-969d-dbd7d6baad90","added_by":"auto","created_at":"2024-12-16 15:39:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":627298,"visible":true,"origin":"","legend":"\u003cp\u003eChemistry of stream water at the observed stations from 2007 to 2024\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/8df972ca7038ca83df74a930.png"},{"id":71550358,"identity":"d19de380-aa56-4477-972d-ed55ffba5aff","added_by":"auto","created_at":"2024-12-16 15:39:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":127896,"visible":true,"origin":"","legend":"\u003cp\u003eLand use change from 2007 to 2021; First column for time series of the land use types in the Chi-Mun River Basin, and Second column for the land use change in time series. a) Paddy, b) Agriculture, c) Farm, d) Aqua agriculture, e) Forest, f) Misc., g) Urban, and h) Water.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/f42b5e98c6c833e2512897fa.png"},{"id":71550379,"identity":"ff8b4952-879a-41ee-99a7-d8afd6f46ca0","added_by":"auto","created_at":"2024-12-16 15:39:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":35938,"visible":true,"origin":"","legend":"\u003cp\u003eThe independent samples t-test for the detection of seasonal differences in water quality indexes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/97821418677ee0b351e59d85.png"},{"id":71550382,"identity":"fcbeaffe-29fa-45d7-af84-741cce38e435","added_by":"auto","created_at":"2024-12-16 15:39:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":281155,"visible":true,"origin":"","legend":"\u003cp\u003eChange of the chemistry in the stream water averaged for all stations in seasonal time series.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/9ab6cdbf596033a2fe6e0937.png"},{"id":71550344,"identity":"ed91dce1-2c9a-41fe-9fa6-4fb5f8b539c5","added_by":"auto","created_at":"2024-12-16 15:39:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53678,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of land use change on water quality estimated by redundancy analysis (RDA) on each land use type.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/32d577a202b5528e4ab47397.png"},{"id":71550391,"identity":"b3e9e9d5-b745-4d19-a5f6-cd2c92b2ebb8","added_by":"auto","created_at":"2024-12-16 15:39:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":105313,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between land use change and water quality parameters at different temporal scales based on a redundancy analysis (RDA).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/bc41dc513fb5a9264f6ae23d.png"},{"id":71552135,"identity":"c368e6f7-5d4a-44bb-8adf-afb9b58a5189","added_by":"auto","created_at":"2024-12-16 15:55:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2988319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5341317/v1/3ea8d452-0fbc-4190-9a4d-98ef847d1289.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Land Use Change on Seasonal Water Quality, Case Study in Chi-Mun River Basin in Thailand","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBoth natural and human-induced factors contribute to changes in water quality, with human activities having a more visible effect. Intensive human activities can affect land use patterns, thereby influencing water quality (Tong et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The quality of water plays an essential role in protecting habitats, supporting agriculture, sustaining industry, and safeguarding public health (Akasaka et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lu et al., 2015). It is challenging to maintain water quality due to the presence of both point source (PS) and non-point source (NPS) pollution. PS pollution primarily includes industrial and domestic wastewater loads, which can be relatively easily identified (Wang et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, NPS typically comes from diffuse sources like agriculture, runoff, and the deposition of atmospheric pollutants (Ongley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Identifying NPS pollution is complicated by the complex interplay of rainfall and landscape characteristics (Liu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As the social economy, industry, agriculture, and urbanization continue to progress, water quality will inevitably decline (Yang et al., 2023).\u003c/p\u003e \u003cp\u003eThe response of the surface water environment to changes in land use patterns has attracted considerable scholarly attention in recent years (Peng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Land use changes have indirect effects on water quality by changing land surface characteristics, affecting regional hydrological cycles, and biochemical cycles. For example, changes in land use led to variations in land cover types and the intensity of land use, which affect the soil's capacity to retain water and soil erosion. These changes impact processes such as evapotranspiration, runoff formation, and groundwater recharge, leading to the accumulation of pollutants (Arcega-Cabrera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Urbanization, for instance, increases surface runoff, reduces evaporation and infiltration, and increases the accumulation of pollutants (Schulte-Uebbing et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the impact of land use types on water quality exhibits marked variations across different spatiotemporal scales (Crites et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe variations in patterns of land use in temporal and spatial, including the characteristics at different scales generate uncertainty in understanding how different types of land use affect the quality of water (Ding et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a result, researchers mainly investigate understanding how water quality responds at different scales, with most existing studies concentrating on sub-basin and riparian scales (Wang et al., 2023a; Zhang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several studies have shown that the types of land use within riparian buffers have a more significant impact on water quality than those at the sub-basin level. However, the previous studies unidentified the specific scale at which riparian buffers most significantly affect water quality, possibly due to differences in location and research approaches (Han et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mainali and Chang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this study examines various spatial scales of sub-basin for analysis. Additionally, significant differences in water quality between dry and wet seasons within the same watershed have been observed (Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The high-intensity rainfall during the wet season can have a considerable impact on water quality in the watershed. This has led to increased research focus on the relationship between seasonal changes in land use and water quality in the river basin (Michalak, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to the complex nature and the effects of changes in land use, the way water quality responds to seasonal variations in types of land use continues to be a topic that requires further investigation.\u003c/p\u003e \u003cp\u003eAt the scale of the study area, most existing studies have pointed from small to medium basins. For instance, research conducted in 1995 on the Bentong River in Malaysia revealed that forests possess strong water purification capabilities (Shu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Wei et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigated the water quality of different lakes and reservoirs in China by considering land use types, while Zhou et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) inspected the correlation between land use and water quality in the Shiyang River Basin on the northern slope of the Qilian Mountains. However, there is relatively less research focusing on large-scale basins and comparative studies between basins. Large-scale basins encompass extensive areas, including multiple regions and cities, as well as diverse landforms and ecosystems. It is crucial to understand the impact of land use patterns on water quality in large-scale basins for water resources management, ecological conservation, and regional development planning. Moreover, given the ongoing societal progress, regional coordinated development is increasingly critical, surpassing the adequacy of single-basin studies. Comparative studies between basins can not only uncover shared geographical processes and rules but also deliver water quality response outcomes tailored to the distinctive characteristics of each basin by integrating their unique natural and social environments (Zhou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies can advance water environmental protection and sustainable development in diverse basins, and offer guidance in formulating cross-regional water quality and land management policies (Ke and Zhang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ouyang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Chi-Mun River Basin in Thailand, which is the largest tributary of the Mekong River Basin, covers a wide area and has diverse natural and socio-economic conditions, leading to varied land use patterns. Moreover, as a result of the area's distinct monsoon and continental climate, including the impact of typhoons, significant seasonal and spatial differences in rainfall are evident in major river basins in Thailand. The Chi-Mun River Basin in northeastern Thailand is heavily influenced by the subtropical monsoon climate, receiving abundant annual precipitation mainly during the rainy season (April\u0026ndash;October). Basins in the northeast are primarily impacted by temperate monsoon and continental climates, with higher annual precipitation averaging around 900\u0026ndash;1,500 mm (Kite, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Serbpongpan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Artlert et al., 2013; Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pawar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis research focused on the Chi-Mun River Basin in Thailand (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and investigated a correlation between changes in land use and water quality on a large river basin scale. This is the first study to investigate the correlation between land use change and water quality parameters for the river basin in Thailand and the Mekong River Basin. The study holds importance in achieving harmonized development across different regions and offers insights for comparative studies on large-scale river basins globally. Thus, the objective of this study is to (1) characterize the spatial and temporal variations of land use change patterns and water quality parameters in the Chi-Mun River basins in Thailand; (2) quantify the correlation between land use change patterns and water quality in the basin; (3) investigate the multi-scale impacts of land use change patterns of basins on water quality parameters.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eThe method, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, aims to address the research question of this study. It is based on three components: land use change estimation, water quality index analysis in seasonal, and relationship analysis between land use change and water quality index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Chi-Mun River Basin, which is the largest tributary of the Mekong River Basin, is situated in the northeastern region of Thailand within the coordinates 14\u0026deg;-16\u0026deg; N, 101\u0026deg;-106\u0026deg;E. This expansive basin covers an area of approximately 120,000 square kilometers, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It contributes around 25,000\u0026nbsp;million cubic meters (MCM) of flow to the Mekong River through an annual runoff of approximately 800 cubic meters per second (m3/s). The majority of this runoff takes place during the rainy season, which spans from April to October, while the dry season is characterized by lower flow rates.\u003c/p\u003e \u003cp\u003eThe rainfall in the river basin is predominantly influenced by the monsoon during the rainy season, with an annual precipitation range of approximately 900 to 1,500 millimeters. The topography of the river basin features mountains along the border and flat terrain in the central area, with elevations ranging from 200 to 2,000 meters. The natural vegetation in the basin primarily consists of forests and shrubs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Land use data\u003c/h2\u003e \u003cp\u003eThe land use change pattern in the Chi-Mun River Basin was estimated by the data that was provided by the Land Development Department (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www1.ldd.go.th/\u003c/span\u003e\u003cspan address=\"http://www1.ldd.go.th/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Thailand. The data was collected from 2007 to 2021 and includes eight land use types: paddy fields, agriculture, farms, aquacultural agriculture, miscellaneous, urban areas, and water (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We used the Quantum Geographic Information System (QGIS 3.34.7-Prizren) to evaluate the importance of the spatial extent of the effects of land use on water quality in the river basin. Paddy revealed the main land use type in the study area, followed by Agriculture, and Forest (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use types from 2007 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eArea each land use type, km2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaddy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAqua.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMisc.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58,542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25,905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54,630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Water quality data\u003c/h2\u003e \u003cp\u003eWater quality data was monitored and provided by the Ministry of Natural Resources and Environment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rwater.mnre.go.th/\u003c/span\u003e\u003cspan address=\"https://rwater.mnre.go.th/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the eleven sampling stations in the Chi-Mun River are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec. The three stations, CI01, CI02, and CI03, are in the Chi River, and the eight stations, MU01, MU02, MU03, MU04, MU05, MU06, MU07, and MU08, are in the Mun River. Water samples were collected once in January, March, May, and August from the year of 2007 to 2024. The time series data of the water quality, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, were split into two seasons, wet and dry. The data for the wet season is in the May and August, and the dry season is in the January and March. To analyze the environmental quality of the surface water presented by eleven parameters: pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal Coliform Bacteria (FCB), Total Phosphorus (TP), Nitrate Nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N), Ammonia-nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N), Suspended Solids (SS), Electrical Conductivity (EC), and Water Quality Index (WQI). The pH average presented approximately 7.3 considering all stations; the pH peak was in 2009. The DO had an average of approximately 3.3 mg/L, the peak of DO was in the wet season of 2007. The BOD was averaged by approximately 1.3 mg/L with a peak in 2015 and 2019. The TCB was 7,000 MPN/100ml as the average value with peaks in 2009, 2010, 2015, and 2017. The FCB had an average value of approximately 0.07 mg/L with a peak at the MU04 station in 2017. The TP was approximately 0.8 of the average value and the peak was approximately 0.1 mg/L at the MU08 station. The NO\u003csub\u003e3\u003c/sub\u003e-N was represented with 0.7 for the average value with the peak in 2013 and 2015 at the CI01, CI02, and CI03 stations. The NH\u003csub\u003e3\u003c/sub\u003e-N was average at 0.35 mg/L with two peaks in 2013 and 2017. The SS presented an average value of 21 mg/L with a peak in 2007 and 2014. The EC was 230 \u0026micro;S/cm with a peak in 2021. The WQI was represented by 70 for the average value for all stations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe water quality data's normality was assessed using the Kolmogorov-Smirnov test. The independent samples t-test was employed to verify seasonal variations in water quality parameter concentrations to compare two independent samples for significant differences. Spatial distinctions among water quality parameters across monitoring stations were examined using one-way analysis of variance (ANOVA) to identify significant differences among multiple independent samples. Redundancy analysis (RDA) is frequently utilized to examine the connections between species variables and environmental variables (Kleyer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We utilized the R platform's RDA library to investigate changes in water quality and land use types across various spatial scales within the river basin (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the RDA analysis plot, two distinct types of arrows symbolize response variables and explanatory variables, respectively. If the angle between the arrows of two variables is acute, they are positively correlated; if the angle is obtuse, they are negatively correlated. Moreover, the angle between the arrows changes inversely with the degree of correlation between the two variables. The impact of each variable on water quality is directly proportional to the length of the arrows. Furthermore, RDA analysis evaluates how much each type of land use explains changes in all water quality parameters (Uwacu et al., 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results aim to answer the research questions based on the method of this study, which was divided into three issues. First, the land use change was used to present the percentage change in land use in the study area during the period from 2007 to 2021. Next, an independent samples t-test was used to present the temporal changes in water quality, demonstrating significant disparities between seasons and offering a detailed illustration of the spatial and temporal changes in water quality. Finally, redundancy analysis (RDA) was used to demonstrate the impact of land use on the change in water quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Land use change\u003c/h2\u003e \u003cp\u003eThe land use in the Chi-Mun River Basin was analyzed from 2007 to 2021. The percentage of each land use type and the changes over time are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. During this period, there was a decrease in the area of paddy fields, farms, forests, and miscellaneous land use. In contrast, there was an increase in agriculture, aqua agriculture, urban areas, and water. In 2007, paddy fields covered approximately 50% of the total area, decreasing to approximately 43% by 2021 with a decreasing rate of approximately 0.4 percent/year, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ea1. Figure\u0026nbsp;5a2 shows the most significant decrease in paddy fields between 2009 and 2010. Agriculture, the second most dominant land use type, increased from approximately 20% in 2007 to approximately 27.5% in 2021 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb1), with an increasing rate of approximately 0.5 percent/year and the highest increase observed in 2016 (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eb2), at approximately 3%. The farm area increased from approximately 0.3% in 2007 to approximately 0.45% in 2009 and decreased to approximately 0.3% in 2021 as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec1, with the most significant approximately 0.16% increase in 2008 and a decrease of approximately 0.06% in 2017 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ec2). The aqua agriculture area increased from approximately 0.1% in 2007 to approximately 0.15% in 2021 as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ed1, with the most significant decrease between 2019 and 2020 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ed2). The forest area decreased from approximately 16.5% in 2007 to about 14.5% in 2021 as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ee1, with a decreasing rate of approximately 0.15 percent/year and the most significant decrease between 2016 and 2017 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ee2). The miscellaneous area increased from approximately 4.0% in 2007 to approximately 4.5% in 2011 and decreased to approximately 4.0% in 2021 as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ef1, with the most significant approximately 0.7% decrease in 2017 and an increase of approximately 0.5% in 2018 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003ef2). Urban areas increased from approximately 5.25% in 2007 to approximately 6.5% in 2021, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eg1, with an increasing rate of approximately 0.1 percent/year and the highest increase observed between 2013 and 2014 at approximately 0.32%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eg2. Finally, water areas increased from approximately 3.25% in 2007 to approximately 3.75% in 2021, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eh1, with the highest increase observed between 2009 and 2010 at approximately 0.24%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eg2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal variation of water quality\u003c/h2\u003e \u003cp\u003eThe results of the independent samples t-test revealed that the levels of pH, BOD, SS, and WQI exhibited statistically significant differences between seasons, with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e provides a comprehensive depiction of the spatial and temporal fluctuations in water quality. Notably, during the dry season, elevated levels of pH, BOD, and NH\u003csub\u003e3\u003c/sub\u003e-N were observed, while DO, TCB, FCB, TP, NO\u003csub\u003e3\u003c/sub\u003e-N, SS, EC, and WQI exhibited higher values in the wet season. It is worth noting that the maximum concentrations of DO, NH\u003csub\u003e3\u003c/sub\u003e-N, and SS were exclusively observed during the wet season of 2007. Over the years from 2007 to 2024, a significant decrease in pH was noted during the dry season, while an increase was observed in the wet season. Additionally, there was a slight increase in the levels of DO, BOD, TCB, NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e3\u003c/sub\u003e-N, and EC during both the dry and wet seasons over the same period. Conversely, FCB, TP, SS, and WQI experienced a noteworthy decrease during the same timeframe for both dry and wet seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ea presented pH values across dry, wet, and annual periods in an average for all stations that generally show stability with values approximately 7.0 to 8.0. The wet season tends to show slightly more variability in comparison to the dry season. The DO presents a gradual upward trend, especially noticeable in the annual and wet season data, with values increasing from around 6 mg/L in the earlier years to approximately 8 mg/L by 2022, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eb. It found that the dry season data is more variable compared to the wet season. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ec reveals the BOD level remained relatively low, generally fluctuating between 0 and 4 mg/L across all three periods. There is some higher variability, particularly in the wet season and annual, with occasional spikes in certain years. TCB displayed considerable variation across the years, particularly in the wet season, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ed. Significant peaks occur during certain years, with TCB values exceeding 20,000 MPN/100 mL, particularly around 2012\u0026ndash;2014. The dry season tended to show lower median values compared to the wet season. FCB followed the pattern of the TCB, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ee, with higher variability in the wet season and showed a peak between 2009 to 2014. A pattern of decay is in both dry and wet seasons after 2015, suggesting an improvement in water quality. TP revealed relatively low with most value of approximately 0.3 mg/L as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ef. Its trend showed a decay, especially in the wet season and annually indicating potential reductions in nutrient pollution over time. The highest variable and concentration levels were presented from 2007 to 2010. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eg presents the NO\u003csub\u003e3\u003c/sub\u003e-N concentration variability across seasons, with a peak occurring from 2010 to 2015, especially in the wet season and annually. The dry season trended to a low value of approximately 0.1 mg/L for occasional spikes in 2014. Overall, the levels of NO\u003csub\u003e3\u003c/sub\u003e-N decreased across all periods after 2016. NH\u003csub\u003e3\u003c/sub\u003e-N was relatively higher in 2015 and presented in the wet season and annually, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eh. The NH\u003csub\u003e3\u003c/sub\u003e-N showed a significant reduction in concentrations for all seasons with a value of approximately 0.5 mg/L after 2016. The dry season showed values lower than the wet season to indicate seasonal impacts on the NH\u003csub\u003e3\u003c/sub\u003e-N level. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ei shows the SS concentrations, particularly during the wet season revealed a high peak in 2007 of approximately 250 mg/L. The SS levels decay after 2015 in stability at a lower level for both the dry and wet seasons. The annual data revealed the same trend, showing an overall decrease in SS after 2015. EC remained relatively stable over time with a value approximately from 150 to 350 \u0026micro;S/cm across all periods, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ej. Slightly higher values were presented during the dry season compared to the wet season. It revealed that small peaks were visible in 2017 and 2021. The WQI value revealed a range from approximately 50 to 100 to suggest that water quality varies from medium to good. The wet season presented slightly more variability than the dry season. Overall, there is a slight improvement in the WQI to indicate a positive trend in the water quality after 2015 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ek). However, the WQI in the wet season was generally lower than in the dry season to consider in the seasonal variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of land use on water quality\u003c/h2\u003e \u003cp\u003eThe redundancy analysis (RDA) method measured the influence of different land uses on water quality throughout different seasons. It identified Farm, Forest, and Urban as the land use types with the most significant impact on water quality during the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). During the wet season, Paddy had the highest effect on water quality, followed by Farm and Urban (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). For the annual period, Paddy, Farm, Forest, and Urban contributed the most to water quality changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). The RDA results showed that the impact of land use types on water quality varied significantly depending on the season. Specifically, the response of water quality to land use was more pronounced during the dry season. Overall, the predictors explained approximately 90% of the variations in water quality across all land use types in the annual period.\u003c/p\u003e \u003cp\u003eThe specific land use types that had the most significant impact on water quality were identified based on their contribution rates. During the dry season, the Farm land use type had a substantial influence, accounting for 20.5% of the impact on water quality. On the other hand, during the wet season, the Paddy land use type greatly affected water quality, contributing to 30.2% of the impact. Together, these two land use types represented 21.9% of the annual impact on water quality. This is attributed to the increased precipitation during the wet season, which washes pollutants from the Paddy areas into nearby water bodies, leading to changes in water quality. These findings are visually represented in Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e, illustrating the varying impact of different land use types on water quality across different seasons.\u003c/p\u003e \u003cp\u003eThe RDA (Redundancy Analysis) plot provides a visual representation of the relationships between different land use types and water quality parameters. In Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the angles between arrows representing different variables reflect the varying correlations between land use types and water quality parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ea corresponds to the dry season, while Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eb corresponds to the wet season. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ec is utilized to present the annual data. Across different spatial and temporal scales, Urban, Agriculture, Aqua agriculture, and Water demonstrate a negative association with pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total phosphorus (TP), ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N), nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N), suspended solids (SS), total coliform bacteria (TCB), and water quality index (WQI) during the dry season. Conversely, Paddy, Forest, Farm, and Miscellaneous exhibit a positive association with fecal coliform bacteria (FCB). Additionally, during the wet season, Paddy, Forest, Farm, and Miscellaneous show a negative association with FCB, SS, DO, and NH\u003csub\u003e3\u003c/sub\u003e-N. Furthermore, Urban, Agriculture, Aqua agriculture, and Water demonstrate a positive association with TCB, NO\u003csub\u003e3\u003c/sub\u003e-N, BOD, and TP. The annual correlation pattern exhibits a direction similar to the wet season.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis discussion explores the complex relationship between land use change and water quality. It looks at how factors such as agriculture, urbanization, and forestry can affect water ecosystems by introducing nutrients. Understanding these relationships is crucial for effective water resource management. The discussion also considers the impact of seasonal variations and long-term trends on water quality, providing important insights into the dynamics of river basins. Additionally, practical suggestions for river basin management are offered, emphasizing the need for integrated strategies that take into account both land use practices and temporal variability to protect water quality and maintain ecological balance.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Relationships between land use change and water quality\u003c/h2\u003e \u003cp\u003eThis study examined the influence of various land use patterns on water quality across different spatial and temporal scales, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Agriculture, Aqua agriculture, Urban, and Water were found to positively affect water quality, while Paddy, Farm, Forest, and Miscellaneous had a negative impact, consistent with previous research findings (Camara et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cole et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Redundancy Analysis (RDA) in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e showed distinct relationships between land use types and water quality parameters during dry, wet, and annual periods.\u003c/p\u003e \u003cp\u003eUrban and Agriculture (cropland) areas exhibited strong positive correlations with EC, WQI, and other pollutants, particularly in regions dominated by agriculture and urbanization in the Chi-Mun basin. The area was characterized by intensive farming and urban expansion, contributing significantly to nonpoint source pollution due to runoff containing excess fertilizers, pesticides, and organic waste from livestock. The urban area also introduced pollutants, including heavy metals and organic matter from industrial and domestic sources, into nearby water bodies (Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Miranda et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, Forest, Miscellaneous, and Water, as a natural area, were associated with improved water quality. These ecosystems negatively correlated with BOD, DO, WQI, and NH\u003csub\u003e3\u003c/sub\u003e-N, indicating their role in filtering pollutants and reducing nutrient loads. If the concentration of inorganic nitrogen surpasses 0.3 mg/L in the spring, it indicates an adequate nitrogen level to facilitate the growth of summer algae blooms (UMassAmherst, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Vegetation can mitigate soil erosion, intercept chemical runoff, and absorb nutrients, while Water enhanced water quality through hydrological regulation and nutrient transformation (Canet-Mart\u0026iacute; et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the natural filtration processes of aquifers and riverbeds help purify water, particularly during low precipitation periods, ensuring a balance in water quality and ecosystem health (Feng et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effect of temporal scale on water quality\u003c/h2\u003e \u003cp\u003eThe RDA results showed that there are variations in the correlation between land use patterns and water quality indicators across the Chi-Mun basins. In the dry season, land use has a more significant impact on water quality compared to the wet season. This is because the undulating terrain in the basins affects the flow rate of surface runoff, which in turn influences the rate of pollutant accumulation, absorption, and transformation, leading to different water quality results in the basins (Bai et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor instance, in the middle and lower parts of the Chi-Mun River, where there are numerous plains and a dense river network, slow water flow in river channels during the dry season leads to the accumulation of pollutants, which negatively affects water quality. On the other hand, the highland areas upstream of the basin have loose soil that is easily eroded by rainwater, significantly affecting water quality in the wet season. Additionally, factors such as precipitation, river flow, and the timing of agricultural cultivation also play a role. Excessive and heavy precipitation can lead to changes in river flow, weakening the regulation of pollutants by wet season land use patterns (Karimi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pinto et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., 2023).\u003c/p\u003e \u003cp\u003eCrop growth and cultivation times vary due to differences in temperature and precipitation, and the use of large quantities of fertilizers and pesticides during the cultivation period results in a rapid increase in pollutant concentrations, leading to significant changes in water quality (Ahmad et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, the contribution of the wet season differs from that of the dry season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Suggestion to river basin management\u003c/h2\u003e \u003cp\u003eThe Northern part of Thailand is experiencing ongoing urbanization, posing significant challenges to the ecological water environment in the Chi-Mun basin. It is crucial to develop effective land management policies and implement ecological protection measures to address these challenges. Vegetation and water play a vital role in improving water quality in the basin, highlighting the need to prioritize their protection in policy formulation. Measures such as returning farmland to forests and establishing vegetation protection zones are essential for enhancing ecological construction within the basin. Agriculture and urban activities have a negative impact on water quality in all basins, but this can be mitigated through careful planning, precise fertilizer application, and proper wastewater treatment before discharge.\u003c/p\u003e \u003cp\u003eIn addition to common characteristics, geographic and socio-economic factors also influence the environment in the basin. During the wet season, soil erosion, surface undulation, and certain land use types have negative impacts on water quality, necessitating better prevention of winds and sand, and reduction of agriculture and urban land use. It is essential to build and improve rainwater management systems to reduce direct urban or agricultural surface runoff into the river. During the dry season, safeguarding water quality is critical, requiring repairs and improvements to the substrate, enhancing the river's self-purification capacity, and reducing agricultural runoff through measures such as water transfer projects and construction of artificial wetlands, as well as adopting efficient water-saving irrigation techniques.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe land use patterns in the Chi-Mun River basin of Thailand have changed in recent years. There has been a decrease in paddy and forest areas, while urban, water, Aqua agriculture, and agricultural land areas have expanded significantly in an increase. The shares of other land use types in the area have fluctuated. Water quality in the basin has shown significant seasonal variations between wet and dry seasons. All water quality indicators have displayed significance among the basins in different seasons, except for EC and WQI concentration in the basin during the dry season, which remained relatively stable. Forest, miscellaneous, and water have demonstrated water purification effects, whereas agriculture and urban areas have exacerbated water quality degradation.\u003c/p\u003e\n\u003cp\u003eThis research investigated the impacts and scale effects of land use on water quality across different time scales in the Chi-Mun basin of Thailand and put forward corresponding recommendations for water quality protection, considering the characteristics of the natural and socio-economic environments of the basin. It aids in the development of rational and effective land management policies and ecological protection measures tailored to the specific conditions of the basin, providing practical importance for the scientific management of water environments and regional coordinated development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKwanchai Pakoksung:\u003c/strong\u003e Writing – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization. \u003cstrong\u003eNantawoot Inseeyong:\u003c/strong\u003e Validation, Investigation\u003cstrong\u003e. Nattawin Chawaloesphonsiya:\u003c/strong\u003e Validation, Methodology, Investigation, Data curation, Writing – review \u0026amp; editing. \u003cstrong\u003ePatiparn Punyapalakul:\u0026nbsp;\u003c/strong\u003eValidation, Methodology, Investigation, Supervision, Data curation, Writing – review \u0026amp; editing. \u003cstrong\u003ePichet Chaiwiwatworakul:\u0026nbsp;\u003c/strong\u003eValidation, Methodology, Investigation, Supervision, Data curation, Writing – review \u0026amp; editing. \u003cstrong\u003eMengzhen Xu:\u003c/strong\u003e Validation, Methodology, Investigation, Supervision, Writing – review \u0026amp; editing. \u003cstrong\u003ePavisorn Chuenchum:\u003c/strong\u003e Writing – review \u0026amp; editing, Writing – original draft, Validation, Project administration, Methodology, Investigation, Supervision, Resources, Funding acquisition, Formal analysis, Data curation, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our deepest gratitude to the Ministry of Natural Resources and Environment, Thailand, for providing the water quality data and the Land Development Department for the land use data essential to this study. We also extend our appreciation to the Department of Water Resources Engineering, Faculty of Engineering, Chulalongkorn University, and Tsinghua University for their technical support and insightful discussions throughout the research process. Finally, we acknowledge the invaluable feedback provided by our reviewers, whose suggestions greatly enhanced the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is funded by Thailand Science Research and Innovation Fund, Chulalongkorn University (No. 6641/2566).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Ministry of Natural Resources and Environment, Thailand, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of the Ministry of Natural Resources and Environment, Thailand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors undertake that this article has not been published in any other journal and that no plagiarism has occurred.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors agree to participate in the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors agree to publish in the journal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTong, S., Li, W., Chen, J., Xia, R., Lin, J., Chen, Y., Xu, C.-Y., 2023. A novel framework to improve the consistency of water quality attribution from natural and anthropogenic factors. J. Environ. 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Rep. 11 (1), 16526. https://doi.org/10.1038/s41598-021-96075-3.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land Use Change, Water Quality, Seasonal Variations, Anthropogenic Impact, Redundancy Analysis (RDA)","lastPublishedDoi":"10.21203/rs.3.rs-5341317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5341317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the correlation between land use changes and water quality in the Chi-Mun River Basin, Thailand, from 2007 to 2021. It is the first of its kind in the region and the Mekong River Basin, providing critical insights for global river basin management. The research analyzes spatial and temporal land use changes and their multi-scale impacts on water quality, utilizing land use change estimation, water quality index analysis, and redundancy analysis (RDA). The results showed that stream water quality variables displayed highly temporal variations, with pH, Biochemical Oxygen Demand (BOD), Total Coliform Bacteria (TCB), Fecal Coliform Bacteria (FCB), Total Phosphorus (TP), Nitrate Nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N), Ammonia-nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N), Suspended Solids (SS) all generally displaying higher levels in the wet season, while there were higher concentrations of Dissolved Oxygen (DO), Electrical Conductivity (EC), and Water Quality Index (WQI) in the dry season. The water samples were collected once in January, March, May, and August from 2007 to 2024. The water quality in wet season is represented in May and August, while in dry season is represented in January and March. The total contribution of land use patterns on overall water quality was stronger during the wet season. It shows a decline in paddy and forest areas alongside an expansion of urban, agricultural, and aqua agricultural land. Water quality displayed significant seasonal variations, with forests and water bodies contributing to purification, while agricultural and urban areas degraded water quality. The findings offer recommendations for water quality protection and land management policies that align with the basin\u0026rsquo;s natural and socio-economic characteristics, promoting coordinated regional development.\u003c/p\u003e","manuscriptTitle":"Impact of Land Use Change on Seasonal Water Quality, Case Study in Chi-Mun River Basin in Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 15:38:47","doi":"10.21203/rs.3.rs-5341317/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d851ecbc-94d7-469c-8870-7031bac2ed6d","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40760846,"name":"Earth and environmental sciences/Environmental sciences"},{"id":40760847,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2024-12-16T15:38:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 15:38:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5341317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5341317","identity":"rs-5341317","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}