Assessing impacts of rainfall intensity and slope gradient on runoff process and dissolved organic carbon loss via surface flow and interflow under simulated rainfall

Assessing impacts of rainfall intensity and slope gradient on runoff process and dissolved organic carbon loss via surface flow and interflow under simulated rainfall | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing impacts of rainfall intensity and slope gradient on runoff process and dissolved organic carbon loss via surface flow and interflow under simulated rainfall Lu Xu, Jun Lu, Dan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4821149/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dissolved organic carbon (DOC) is an indispensable component of the global carbon cycle and potentially affects aquatic ecosystems. Previous research on runoff process and DOC loss mainly focused on surface flow, with few reports of the hydrological pathway of interflow or DOC loss via interflow. To address this deficiency, a series of rainfall simulations were conducted with three rainfall intensities of 60, 90, and 120 mm h − 1 (R60, R90, and R120) and three slope gradients of 5, 15, and 25° (S5, S15, and S25) of purplish soil. The initial time of surface flow was faster under high rainfall intensity and steep slope, and the initial time of interflow increased with increased rainfall intensity under gentle slope. In general, the surface flow rates increased first, and reached a steady state within 10–35 min. The interflow curves were single-peak curves for R60-S5 and R90-S5, but exhibited a continued rising trend for other treatments. The interflow volume occupied 69.2% of the total runoff volume under R60-S5, and the percentages of interflow decreased as the rainfall intensity and slope increased. These results indicated that interflow was an important hydrological pathway in this purplish soil area. The DOC concentration of the surface flow decreased with rainfall duration, with opposite trend for DOC concentration of interflow. The DOC concentrations in the interflow were 1.35–2.34 times higher than those in the surface flow. However, the rainfall intensity and slope had little effect on DOC concentrations in both surface flow and interflow. Furthermore, the DOC loss fluxes via surface flow and interflow were 3.77–26.94 g and 0.41–13.73 g, respectively, and the ratios of interflow DOC loss fluxes to the total DOC loss fluxes gradually decreased with the increase of rainfall intensity and slope. Under R60, DOC loss via interflow was the major DOC loss pathway, accounting for 51.0%-78.4% of the total DOC loss, whereas for R90 and R120, DOC loss via surface accounted for > 90%. Moreover, runoff volume was positively linearly correlated with the corresponding DOC loss fluxes in both the surface flow (R 2 = 0.93, P < 0.01) and interflow (R 2 = 0.99, P < 0.01). These results contribute to our understanding of the relationship between carbon cycle and soil erosion, and provide a scientific basis to estimate the fluxes of DOC loss and controll carbon loss in the purplish soil area of China. Earth and environmental sciences/Hydrology Earth and environmental sciences/Ecology/Biogeochemistry Earth and environmental sciences/Solid earth sciences/Hydrogeology Earth and environmental sciences/Biogeochemistry/Carbon cycle Hydrological pathway Dissolved organic carbon Rainfall intensity Slope gradient Simulated rainfall Purplish soil 1. Introduction Soil organic carbon (SOC) plays an essential role in maintaining soil quality, land ecosystem services, and the carbon cycle (Lal, 2003 ; Dawson et al., 2007; Doetterl et al. 2016 ). Soil erosion, especially water erosion, can lead to soil loss but can also affect dynamic changes of SOC (Lal and Pimentel et al., 2008; Berhe et al., 2018 ), resulting in declining land productivity, increasing nonpoint-source pollution, and global warming (Kindler et al., 2011 ; Agata et al., 2015 ). The International Carbon Cycle Planning Organization (IPCC) estimated that globally, soil erosion has caused the redistribution of approximately 75 billion tons of soil and loss of 1–5 billion tons of SOC each year (Yue et al., 2016 ). Previous studies have mostly considered the dynamic changes of particulate organic carbon (POC) associated with sediment (Li et al., 2017 ; Liu et al., 2018 ; Wang et al., 2019a ), but dissolved organic carbon (DOC) associated with runoff is also an indispensable component of global carbon budgets (Lal, 2003 ; Song et al., 2011 ; Li et al., 2019 ). In addition, previous study of soil DOC dynamics mainly focused on migration driven by surface flow (Mailapalli et al., 2012; Gaelen et al., 2014 ; Ma et al., 2014 ; Lei et al., 2018; Li et al., 2019 ). However, for well-developed interflow soils, the hydrological pathway of DOC loss via interflow cannot be ignored in evaluating DOC loss fluxes (Hua et al., 2016 ; Song et al., 2019 ). Thus, studies are needed to quantify the DOC loss from both surface flow and interflow. Previous studies have demonstrated that the DOC loss fluxes via interflow account for a large proportion of the total DOC loss in runoff. For example, Li et al. ( 2018 ) identified that interflow as a crucial route for lateral DOC transport in the rainy season in the sloping land of Sichuan Purplish Soil region, and the annual loss fluxes of DOC by interflow for three years were 565.5, 802.1, and 1090.1 mg·m − 2 . Hua et al. ( 2016 ) conducted a free-drain lysimeter experiment of Regosols in Southwest China, and found an annual loss flux of DOC through interflow of 865.5 mg·m − 2 , which was 92 and five times higher than DOC loss fluxes through sediment and surface flow, respectively. Many factors affect the characteristics of soil DOC loss, including rainfall intensity, slope gradient, soil type, soil properties and agricultural land management (Jin et al., 2009 ; Song et al., 2011 ; Li et al., 2019 ). Li et al. ( 2017 ) studied four typical loess soils and found that sediment and surface flow are the main carriers of SOC, accounting for SOC losses of up to 90%. However, Wang et al. ( 2019b ) and Fu et al. ( 2012 ) reported that interflow, rather than surface flow, could be the main pathway of nutrient loss for purplish soil and weathered granite slopes. Gaelen et al. (2104) identified rainfall intensity as the main factor controlling DOC loss with runoff because it is the main driving force of runoff. Ma et al. ( 2014 ) found that rainfall intensity had a bigger effect on DOC lateral transport than on DOC vertical mobilization, and the DOC loss increased with increased intensity rainfall in the red soil region of Hunan province in China. Slope gradient also exerts strong effects on DOC loss (Martínez-Mena et al., 2102). Fei et al. ( 2019b ) carried out artificially simulated rainfall experiments for red soils, finding that the total organic carbon loss of surface flow first increased and then decreased with increasing slope gradients. However, there was a continued rising trend as slope gradients increased in interflow. Wang et al. ( 2019b ) found that nitrogen loss in the surface runoff and sediment yield increased as the slope gradients increased, and found the opposite trend regarding nitrogen loss in interflow. Purplish soil, covers a total area of 1.6×105 km 2 in the Sichuan Basin, in southwestern China, and is one of the most important soil types for agricultural production in the upper reaches of the Yangtze River (Wang and Zhu et al., 2011; Bah et al., 2020 ). Purplish soil is classified as Eutric Regosols (according to FAO Soil Taxonomy) and is developed from the products of the rapid weathering of underlying sandstones, siltstones, and mudstones. This weathered soil is characterized by a thin soil layer, low organic carbon, and high soil erodibility. Due to the relative thin soil layer and typical “soil-bedrock” dual structure, the soil layers can be easily saturated by rainfall (Jin et al., 2021 ). These soils also have high permeability, with extensive interflow through the “soil-bedrock” interface during the rainy season (Wang and Zhu et al., 2011; Hua et al., 2014 ). Most studies of Purplish soil were carried out in the field under natural rainfall conditions, limiting conclusions about the lateral transport of DOC via surface flow and interflow due to the many uncontrollable factors of natural rainfall. To more carefully probe the contributions of surface flow and interflow to DOC loss, we conducted rainfall simulation experiment indoors with controlled rainfall intensity and slope gradient. The objectives of this study were as follows: (1) to investigate the dynamic changes of runoff rate and DOC concentration; (2) to quantify the distribution of DOC loss via surface flow and interflow; (3) to discuss the influence mechanism of rainfall intensity and slope gradient on DOC loss. 2. Materials and methods 2.1. Soil collection and preparation The soil used in this study was collected from Liangshan Yi Autonomous Prefecture (26°33′42″N, 102°27′53″E), in the southwest of Sichuan Province, China (Fig. 1 ). This region is dominated by a typical subtropical monsoon climate (Tang et al., 2015 ), with an average annual temperature of 16.2°C, and mean annual precipitation of 1111.6 mm, with about 95% of precipitation concentrated in the summer rainy season (Bah et al., 2020 ). This region is primarily purplish soil, with 35 ~ 45 cm soil tillage layer thickness. Soil samples were collected every 10 cm from the surface to 40 cm deep, and soil bulk density was determined by the cutting ring method. The soils from different layers were packed separately and transported to the laboratory. To maintain the natural state of the soil, no sieving was used (Xu et al., 2017 ). We mixed the soil material of the same layer thoroughly, removed the coarse stones and organic debris, and then air-dried the tested soil to achieve a water moisture of 10%±1%. The basic physicochemical properties of the tested soil are displayed in Table 1 . Table 1 Basic physicochemical properties of the tested soil pH BD (g m − 3 ) SOC (g kg − 1 ) TN (g kg − 1 ) TP (g kg − 1 ) TK (g kg − 1 ) AN (mg kg − 1 ) AP (mg kg − 1 ) AK (mg kg − 1 ) Particle size (%) Clay Silt Sand 8.42 1.31 10.09 1.06 0.79 20.22 35.13 28.38 120.00 12.5 62.3 25.2 BD: Soil bulk density, SOC: soil organic carbon, TN: total nitrogen, TP: total phosphorus, TK: total potassium, AN: available nitrogen, AP: available phosphorus, AK: available potassium. 2.2. Soil flume and preparation A three-dimensional soil flume (2.00 × 1.00 × 0.50, length × width × height) was constructed of steel sheets (Fig. 2 ). The slope gradient of the soil flume can be adjusted from 0–30° by hydraulic devices. The bottom of the soil flume is sealed to simulate a natural impermeable layer of a “soil-bedrock” interface. A “V-shaped” trough installed at the downslope edge of the flume was used to collect the surface flow and sediment, and the outlet at the bottom of the soil flume was used to collect interflow. Before filling the soil flume, a 5-cm layer of thick gravel and then gauze were put on the bottom of the flume. Next, a 35-cm soil layer was put on top of the gravel layer. To ensure the uniformity of the soil layer, the soil applied in 5-cm increments, with a soil bulk density of 1.3 g cm − 3 . In total, seven successive 5-cm soil layers were added to the soil flume. During filling, the soil was compacted to reduce the side-wall effect. For each layer, the surface was smoothed and then roughened to allow the adjacent two layers to tightly combine. Additionaly, new soil was used for each rainfall simulation event, rather than keeping the same soil in the flume for a series of rainfall simulation events. 2.3. Rainfall simulator To make the raindrop size and the distribution of the simulated rainfall closely resemble that of natural rainfall, a down sprinkler artificial rainfall simulator system (NYJL-10, Nanjing Forestry University) was employed in this study. The rainfall simulator included twelve sets of rainfall shower heads (three nozzle per head), and rainfall was applied at a rainfall height of 6 m. By changing both nozzle size and water pressure, the available rainfall intensity could be adjusted from 10 to 240 mm h − 1 with uniformity of 88%. Prior to the rainfall simulation experiment, the rainfall intensities were calibrated using a gauge and four rainfall barrels distributed around the soil flume. 2.4. Laboratory rainfall simulation experiments The rainfall simulation experiments were carried out using the soil flume and rainfall simulator in the Key Laboratory of Mountain Hazards and Surface Process, Chengdu City, China. According to the classification of farmland of slopes (Comprehensive Scientific Expedition, 1990), three typical slope gradients of 5 (S5), 15 (S15), and 25° (S25) were selected, representing slight, steep, and the steepest slopes, respectively. Three rainfall intensities of 60 (R60), 90 (R90), and 120 mm h − 1 (R120) were selected, all within the range of typical erosive rainstorms in the Sichuan Basin. A total of 27 simulated rainfalls were conducted, with three rainfall intensities and three slope gradients, with three replicates of each treatment. Prior to performing the experiment, the soil flume was subjected to pre-rainfall with 30 mm h − 1 intensity and 0° slope gradient until achieving continuous interflow. After pre-saturation, the soil flume was adjusted to the experimental slope. To prevent water evaporation, a plastic sheet was used to cover the soil flume and stand for 24 h. During the rainfall simulation, the initial times of surface flow and interflow were first recorded. The surface flow and interflow samples were collected at the corresponding outlets every 2 min. The water samples were volumetrically measured, passed through a 0.65 µm filter membrane, and then stored at 4°C for subsequent DOC analysis. 2.5. Laboratory analysis The basic physicochemical properties of the tested soil, including soil bulk density; pH; soil organic carbon; total nitrogen, phosphorus, and potassium; available nitrogen, phosphorus, and potassium; and the distribution of particle size were all measured by China agrochemical analysis (Bao, 2000 ). The dissolved organic carbon of water samples was determined using a TOC analyzer (Elementar Analysensysteme GrnbH, Germany). 2.6. Data analysis The total DOC loss fluxes during a rainfall simulation event were calculated as follows: \(Q=\frac{{\sum\limits_{{i=1}}^{n} {{\text{2}} \times {C_i}} \times {R_i}}}{{{\text{1000}}}}\) (1) Where Q is the total DOC loss flux of surface flow or interflow in one rainfall simulation, g m − 2 ; C is the DOC concentration of surface flow or interflow, mg L − 1 ; R is the runoff rate of surface flow or interflow, L m − 2 min − 1 ; n is the number of water samples of surface flow or interflow in one rainfall simulation. All statistical analyses were performed using the software package SPSS 20.0, and all the figures were prepared using Origin 9.0 software. Treatment differences were determined using a one-way analysis of variance (ANOVA) at the significance level of 0.05. Multiple linear regression was applied to investigate the relationships between DOC loss and rainfall intensity and slope gradient. Pearson's correlations were used to study the correlations among the measured variables. 3. Results 3.1. Surface flow and interflow loss The runoff initiation times of surface flow and interflow ranged from 0.85 to 4.47 min, and 2.16 to 3.70 min, respectively (Table 2 ). Both rainfall intensity and slope gradient exhibited accelerating effects on surface flow generation. For R60-S5 and R60-S15, the runoff initiation times of interflow were earlier than that of the surface flow, but under R90 and R120, the runoff initiation times of interflow lagged behind that of the surface flow. Table 2 The characteristics of runoff under different rainfall intensities and slopes Rainfall intensity (mm h − 1 ) Slope gradient(°) Initiation time of surface flow(min) Initiation time of interflow (min) Runoff rate of surface flow (L m − 2 min − 1 ) Runoff rate of interflow (L m − 2 min − 1 ) 60 5 4.47 2.16 0.27 ± 0.05c 0.61 ± 0.06a 15 3.21 2.87 0.45 ± 0.03c 0.34 ± 0.12b 25 2.31 2.50 0.48 ± 0.06c 0.22 ± 0.05b 90 5 1.58 2.93 1.19 ± 0.15b 0.35 ± 0.08b 15 1.15 3.70 1.50 ± 0.21ab 0.04 ± 0.03c 25 0.85 3.50 1.48 ± 0.19ab 0.01 ± 0.02c 120 5 1.01 3.67 1.62 ± 0.15ab 0.02 ± 0.04c 15 0.87 3.16 1.68 ± 0.15ab 0.02 ± 0.04c 25 1.20 2.36 1.95 ± 0.16a 0.01 ± 0.05c Note: different lowercase letters indicate significant differences at the P < 0.05 level. The surface flow rate gradually increased with the rainfall duration, and then became stable with slight fluctuations (Fig. 3 ). The time required to reach steady surface flow rate differed with different rainfall intensities and slopes. For low rainfall intensity or relative gentle slope (R60-S5, R60-S15, and R90-S5), the surface flow rates stabilized within 30–35 min, but for large rainfall intensity (R90 and R120) or steep slope gradient (S25), the surface flow rates sharply increased and stabilized within 10 minutes. The surface flow rates increased as the rainfall intensity increased. On average, the surface flow rates of R60 (0.27–0.48 L m − 2 min − 1 ) were significantly lower than those of R90 (1.19–1.50 L m − 2 min − 1 ) and R120 (1.62–1.95 L m − 2 min − 1 ) ( P 0.05, Table 2 ). Additionally, the slope gradient had no significant impact on surface flow rate, although the surface flow rate increased with the increase of slope. The interflow curves were single-peak curves, showing trends of increasing initially and then decreasing under R60-S5 and R90-S5; the interflow rate gradually increased with rainfall duration under other treatments (Table 2 ). The interflow rates decreased with increased rainfall intensity, in the order of R60 (0.22–0.61 L m − 2 min − 1 ) > R90 (0.01–0.35 L m − 2 min − 1 ) > R120 (0.01–0.02 L m − 2 min − 1 ). The slope gradient had little effect on surface flow rates under high rainfall intensity. 3.2. DOC concentration in surface flow and interflow There are two pathways for DOC loss by runoff, namely, surface flow and interflow. Under identical rainfall intensities and slope gradients, the hydrological characteristics of surface flow and interflow are very different, with significant differences in DOC loss (Fei et al., 2019b ). As shown in Fig. 4 , the DOC concentration curves of surface flow peaked early and decreased gradually with the rainfall duration. The DOC concentration of surface flow decreased with the increase of rainfall intensities and showed no difference for different rainfall intensities. The DOC concentrations of surface flow of R60, R90, and R120 ranged from 13.34 to 26.61 mg L − 1 , from 11.56 to 29.25 mg L − 1 and from 10.11 to 22.44 mg L − 1 , respectively (Table 3 ). The average DOC concentration of surface flow at the slope of 15° was always the highest, with 18.37 mg L − 1 at 60 mm h − 1 , 17.84 mg L − 1 at 90 mm h − 1 , and 17.00 mg L − 1 at 120 mm h − 1 . Table 3 The DOC concentrations and the DOC loss fluxes in the surface flow and interflow for different rainfall intensities and slope gradients Rainfall intensity (mm h − 1 ) Slope gradient(°) DOC concentration in surface flow(mg L − 1 ) DOC concentration in interflow(mg L − 1 ) Surface flow DOC loss fluxes(g) Interflow DOC loss fluxes(g) 60 5 16.11 ± 2.88a 23.21 ± 1.57bc 3.77 ± 0.18d 13.73 ± 0.71a 15 18.37 ± 2.09a 24.81 ± 0.97bc 6.94 ± 0.31d 8.22 ± 0.35b 25 16.82 ± 1.98a 29.67 ± 1.53ab 6.29 ± 0.53d 6.55 ± 0.27c 90 5 15.22 ± 1.89a 22.11 ± 0.79c 17.51 ± 0.76c 8.11 ± 0.52b 15 17.84 ± 2.33a 25.71 ± 0.94bc 25.70 ± 1.42ab 1.03 ± 0.08d 25 14.20 ± 2.01a 29.56 ± 1.40ab 23.23 ± 0.97b 0.41 ± 0.06d 120 5 12.91 ± 2.36a 27.46 ± 1.22b 19.69 ± 1.34c 0.42 ± 0.02d 15 17.00 ± 1.29a 25.62 ± 1.02bc 26.94 ± 1.01a 0.55 ± 0.03d 25 13.96 ± 1.09a 32.73 ± 1.27a 25.34 ± 0.37ab 0.44 ± 0.01d Note: different lowercase letters indicate significant difference at the p < 0.05 level. The DOC concentration curves of interflow showed the opposite trends from the surface flow curves, and gradually increased with the rainfall duration (Fig. 4 ). The concentration ranges of R60, R90, and R120 were 17.29–34.89 mg L − 1 , 14.32–39.93 mg L − 1 , and 16.75–41.71 mg L − 1 , respectively (Table 3 ). For the low slope of S5, the average interflow DOC concentration at 120 mm h − 1 was 27.46 mg L − 1 , significantly higher than that of R90. For the high rainfall intensity of R90 and R120, the slope gradient has a significant effect on the interflow DOC concentration. The DOC concentration of interflow increased when rainfall intensity increased, and the average DOC concentration of interflow at the slope of 25° was always the highest, with 29.56 mg L − 1 at 90 mm h − 1 , and 32.73 mg L − 1 at 120 mm h − 1 . 3.3. Distribution of DOC loss fluxes via surface flow and interflow The DOC loss fluxes via surface flow for 60, 90, and 120 mm h − 1 were 3.77–6.94 g, 17.51–25.70 g, and 19.69–26.94 g, respectively (Table 3 ), and the corresponding DOC loss fluxes via interflow were 6.55–13.73 g, 0.41–8.11 g, and 0.42–0.55 g, respectively. As the rainfall intensity and slope gradient increased, the DOC loss fluxes of surface flow increased. However, the opposite effect was seen for rainfall intensity and slope gradient on DOC loss fluxes of interflow. The highest DOC loss flux of the surface flow was 26.94 g for R120-S15 and smallest was 3.77 g for R60-S5. For the DOC loss fluxes of the interflow, the greatest was 13.73 g for R60-S5 and the smallest was 0.42 g for R120-S5. In addition, Fig. 5 displays the ratios of surface flow and interflow DOC loss fluxes to the total DOC loss fluxes. Rainfall intensity and slope gradient had significant effects on the DOC loss distribution. The ratios of interflow DOC loss fluxes to the total DOC loss fluxes gradually decreased with the increase of rainfall intensity and slope gradient. For R60, interflow DOC loss percentages were 78.4%, 54.2%, and 51.0% at 5°, 15°, and 25°, respectively, all > 50%. For R120, the interflow DOC loss percentages were only 2.1%, 2.0%, and 1.7% at 5°, 15°, and 25°, respectively. 3.4. Influence factor correlation coefficient for DOC loss in surface flow and interflow Multiple linear regression equations were developed among slope, rainfall intensity and DOC loss fluxes for surface flow and interflow (Table 4 ). Surface flow DOC loss fluxes were positively correlated with rainfall intensity and slope (R 2 = 0.787, P < 0.01), however, interflow flow DOC loss fluxes were negatively correlated with rainfall intensity and slope (R 2 = 0.822, P < 0.01). The partial correlation coefficients of rainfall intensity and slope were 0.881 and 0.426 for surface flow, and the corresponding absolute values of the partial correlation coefficients for interflow were 0.883 and 0.718. This indicated that the rainfall intensity has a greater impact on the DOC loss fluxes for both surface flow and interflow. Pearson analysis was performed and the correlations among rainfall intensity, slope gradient, runoff volume, and DOC loss via surface flow and interflow are displayed in Table 5 . For surface flow, rainfall intensity and runoff volume were significantly correlated with DOC loss, with the highest correlation coefficient between runoff volume and DOC loss, 0.966, but insignificant correlations were seen between slope gradient and DOC loss. For interflow, rainfall intensity and slope gradient were negatively correlated with DOC loss, and runoff volume was positively correlated with DOC loss, with a correlation coefficient of 0.995. Additionally, the correlation between the three factors and DOC loss via surface flow and interflow were in the order of runoff volume > rainfall intensity > slope gradient. Table 4 Regression equations for slope gradients (S), rainfall intensities, (I) and DOC loss in the surface flow and interflow Runoff type Regression equation R 2 Partial correlation coefficient Slope(°) Intensity(mm h − 1 ) Surface flow Y = 0.232S + 0.305I-13.686 0.787 0.426 0.881 Interflow Y=-0.248S-0.15I + 21.643 0.822 -0.718 -0.883 Table 5 Correlation analysis of slope, rainfall intensity and runoff with DOC loss in surface flow and interflow Factors Surface flow DOC loss(g) Interflow DOC loss(g) Slope(°) 0.217 -0.436* Rainfall intensity(mm h-1) 0.860** -0.795* Runoff volume(L) 0.966** 0.995** **Indicates significant correlation at 0.01, *Indicates significant correlation at 0.05. 4. Discussion 4.1. Surface flow and interflow loss Previous studies have shown that rainfall intensity and slope gradient are the most important factors affecting surface flow and interflow (Fu et al., 2016 ; Wang et al., 2019b ; Wang et al., 2014 ). For the high rainfall intensity of R90 and R120 and steep slope gradient of S25, the surface flow rates sharply increased with the rainfall duration, and reached a steady state after a short-term increase. For R60-S5, R60-S15, and R90-S5, the surface flow rates slowly increased, which lasted a long-term, and reached a steady state until the end of rainfall (Fig. 3 ). These results suggest that excess infiltration may be the main mechanism of surface flow under large rainfall intensity and steep slope gradient, but saturation may be the main cause of surface flow under low rainfall intensity and gentle slope gradient, results that are consistent with those of Li et al. ( 2017 ). In this study, there are different trends of interflow rate for different rainfall intensities and slope gradients (Fig. 3 ). Petry et al. ( 2002 ) reported that interflow includes preferential flow and matrix flow. Thus, further work should investigate the respective contributions of these two kinds of flow. In addition, defining the initial runoff time of the purplish soil and its influencing factors should increase our understanding of the different mechanisms of runoff generation (Liu et al., 2018 ; Deng et al., 2020) The initial time of surface flow increased with the increase of rainfall intensity and slope (Table 2 ), but the change in the initial time of interflow was more complex. We concluded that rainfall intensity is the main factor affecting the initial time of interflow if the slope gradient is low. For a slope of 5°, the initial time of interflow is prolonged with increasing rainfall intensity. This finding is consistent with the results of Xin et al. ( 2008 ) who studied the purplish soil area in southwest China. The observed distribution of surface flow and interflow indicates that rainfall intensity and slope gradient seriously affect the hydrological characteristics of purplish soil slopes. As shown in Fig. 6 , the amount of interflow comprised a larger proportion of the total runoff volume under conditions of lower rainfall intensity and slope. Specifically, the interflow percentage under R60-S5 was the highest, 69.2%, and the interflow percentages decreased as the rainfall intensity and slope gradient increased, with 0.9–69.2%, 0.13–43.25%, and 0.7–31.9% for R60, R90, and R120, respectively. Hua et al. ( 2016 ) suggested that this phenomenon can be explained by water infiltration. Maïga-Yaleu et al. ( 2013 ) proposed that the greater the rainfall intensity, the stronger the impact of raindrops, so the splash of raindrops can cause the sediment particles to clog the topsoil and form a crust that is resistant to water infiltration. Thus, the interflow would be low under high intensity. Additionally, Wang et al. ( 2019b ) and Morbidelli et al. ( 2016 ) also reported that the infiltration rate decreased as the slope increased. However, the results differ from those of Fei et al. ( 2019a ), who reported that the interflow comprised the majority of the total runoff volume at steeper slopes, while the surface runoff comprised most of the runoff for gentle slope gradients of red soil. These differences may reflect differences in the soil types due to varied physical and chemical properties of the soil (Deng et al., 2020). 4.2. DOC loss via surface flow and interflow Nutrient loss through runoff is mainly affected by the amount of runoff volume and the solubility of nutrients in the runoff (Lei et al., 2018; Li et al., 2019 ). Figure 4 shows the trends of DOC concentration in the surface flow and interflow for different rainfall intensities and slope gradients. Opposite trends of DOC concentration were observed for the surface flow and interflow, consistent with the finding of Wang et al. ( 2019b ) that nitrogen concentration in the surface flow decreased with rainfall duration, but increased in the interflow. For surface flow, a large amount of soil organic carbon in the topsoil can be eroded away with increased rainfall duration, resulting in a gradual decrease of DOC concentration in surface flow, called a “flush effect” (Hathaway et al., 2012 ). For interflow, at the start of rainfall simulation, the interflow was dominated by preferential flow, with the macropores in the soil profile serving as channels of preferential flow lacking soil organic carbon (Cey et al., 2010). Then the contribution of matrix flow gradually increased with rainfall duration, and adequate contact time between the infiltration water and the soil increased the dissolution of soil organic carbon in the interflow. No significant differences were found in the surface flow-associated DOC concentration among the different rainfall simulations ( P > 0.05), suggesting only small effects of rainfall intensity and slope gradient on the DOC concentration of surface flow (Table 3 ). The influence of the slope factor on the DOC concentration of the surface flow has been controversial (Liu et al., 2015 ; Li and Wang et al., 2016). In this study, the DOC concentration of surface flow was highest for a slope of 15°, 17.00-18.37 mg L − 1 . These results are similar to the findings by Wu et al. ( 2018 ) that the surface flow velocity increased with increased slope gradient, and the finding of an increase in the dissolution of soil nutrients. However, at a critical slope gradient, the erosion transport capacity would decrease with increased slope gradient. Fei et al. ( 2019a ) explained that this switch could be attributed to breakdown of soil aggregates. For a gentle slope, raindrops can damage small soil aggregates, releasing soluble organic matter to increase DOC concentration of runoff. With a steeper slope gradient, large aggregates with lower soil organic carbon content tend to move more, thereby decreasing the DOC concentration of the surface flow (Liu et al., 2019 ). No significant differences in interflow DOC concentrations were also observed for the different rainfall intensities. Overall, for both surface flow and interflow, the rainfall intensity had little effect on DOC concentration, consistent with the findings of Jin et al ( 2009 ). We also found that the effect of slope gradient on DOC concentration of interflow was only significant with a rainfall intensity of 120 mm − 1 with the lowest interflow DOC concentration for the 15° slope. This is because a large proportion of the topsoil enriched in organic carbon is eroded away by surface flow, with dissolving and infiltration of only a small fraction of the soluble organic carbon into the soil, resulting in a low concentration of interflow. As shown in Table 3 , the DOC loss fluxes of surface flow ranged from 3.77–26.94 g, and those of interflow varied from 3.77–26.94 g. For R60, the DOC loss fluxes of interflow were 78.4%, 54.2%, and 51.0% of total DOC loss at 5°, 15° and 25°, respectively.These results indicated that the interflow is a crucial route of DOC loss under low rainfall intensity and low slope gradient, so nutrient loss through this pathway is significant for in purplish soil area, a finding that is consistent with that of Hua et al. ( 2014 ). In contrast, Kindler et al. ( 2011 ) argued that the DOC loss caused by interflow was comparatively small. This is because that DOC transport associated with interflow is a complex hydrological process that is determined by the amount of DOC in soil, the adsorption and desorption processes of DOC, and the physical and chemical properties of the soil (Fujii et al., 2009 ; Hua et al., 2016 ). 4.3. Influence factor correlation coefficient for DOC loss in surface flow and interflow The DOC loss fluxes can be influenced by many factors, including rainfall intensity, slope gradients, surface runoff, and interflow (Xing et al., 2016 ). According to Pearson analysis (Table 5 ), the influence factor correlation coefficients for DOC loss in surface flow and interflow were ranked as follows: runoff volume > rainfall intensity > slope. Additionally, runoff volume exhibited a positive linear correlation with corresponding DOC loss fluxes in the surface flow (R 2 = 0.93, P < 0.01) and the interflow (R 2 = 0.99, P < 0.01), respectively (Fig. 7 ). Thus, to control DOC loss via runoff, runoff volume should first be considered, followed by rainfall intensity and slope gradient. Previous studies showed that soil and water conservation practices, including contour ridges, straw mulching, and canopy interception of precipitation, can effectively reduce the amount of surface flow by promoting water infiltration (Mailapalli et al., 2012; Xu et al., 2017 ; Jia et al., 2019 ; Chen et al., 2020 ). However, at the same time, the volume of interflow probably increased in well-developed interflow soils (Yu et al., 2020 ). Here, the DOC concentrations in the interflow were 1.35–2.34 times higher than those of the surface flow (Table 3 ), suggesting that reducing the amount of surface flow may increase the risk of DOC loss through interflow under unreasonable measures. Therefore, additional research is needed to investigate the effects of prevention and control practices to reduce nutrient loss in purplish soil area. 5. Conclusions Rainfall simulation experiments of purplish soil were conducted under three rainfall intensities (60, 90, and 120 mm h − 1 ) and three slope gradients (5, 15 and 25°) to investigate runoff and DOC loss processes. The results showed that increasing the rainfall intensity and slope significantly accelerated surface flow generation and increased the surface flow rates, but delayed interflow generation and decreased interflow rates. The DOC concentration in the surface flow decreased with rainfall duration, and the opposite trend was seen for DOC concentration in interflow. For both surface flow and interflow, the rainfall intensity and slope gradient had little effect on DOC concentration, while these two factors affect DOC loss fluxes mainly by affecting the runoff rate of surface flow and interflow. Thus, the effects of rainfall intensity and slope gradient on DOC loss fluxes via surface flow and interflow were similar to the effects on the runoff rate of surface flow and interflow, that is, DOC loss fluxes of surface flow increased with increased rainfall intensity and slope gradient, and DOC loss fluxes of interflow decreased. In addition, interflow is a major hydrological pathway of DOC loss under low rainfall intensity and gentle slope gradient, but surface flow is more important under high rainfall intensity and steep slope gradient. Overall, the results indicate the lateral transport of DOC via interflow should be further studied to control carbon loss and enhance carbon sequestration in purplish soil area. Declarations Author Contribution Methodology, data collection, data analysis, writing—original draft preparation, L.X.; supervision, modification, D.Z.; supervision, J.L.All authors have read and agreed to the published version of the manuscript. Acknowledgements The research reported in this manuscript is funded by the Key Project of China National Tobacco Coporation Sichuan (Grants No. SCYC201802) and CAS President’s International Fellowship Initiative (Grants No. 2020VBB0008). Data Availability Data is provided within the manuscript or supplementary information files References Agata, N. et al. Effectiveness of carbon isotopic signature for estimating soil erosion and deposition rates in Sicilian vineyards. Soil. Tillage Res. 152 , 1–7 (2015). Bah, H. et al. Effects of organic amendment applications on nitrogen and phosphorus losses from sloping cropland in the upper Yangtze River. Agric. Ecosyst. Environ. 302 , 107086 (2020). Bao, S. Analysis of soil and agricultural chemistry (Chinese Agriculture, Beijing, 2000). (In Chinese). Berhe, A. A., Barnes, R. T., Six, J. & Marín-spiotta, E. Role of soil erosion in biogeochemical cycling of essential elements: carbon, nitrogen, and phosphorus. Annu. Rev. Earth Planet. 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The transport of aggregates associated with soil organic carbon under the rain-induced overland flow on the Chinese Loess Plateau. Earth Surf. Process. Landf. 44 , 1895–1909 (2019). Ma, W. et al. Effect of soil erosion on dissolved organic carbon redistribution in subtropical red soil under rainfall simulation. Geomorphology . 226 , 217–225 (2014). Maïga-Yaleu, S. et al. Soil crusting impact on soil organic carbon losses by water erosion. Catena . 107 , 26–34 (2013). Mailapalli, D. R. Infiltration, runoff, and export of dissolved organic carbon from furrow irrigated forage fields under cover crop and no-till management in the arid climate of California. J. Irrig. Drain. Eng. 138 , 35–42 (2012). Martínez-Mena, M. et al. Organic carbon enrichment in sediments: effects of rainfall characteristics under different land uses in a Mediterranean area. Catena . 94 , 36–42 (2012). Morbidelli, R. et al. Laboratory investigation on the role of slope on infiltration over grassy soils. J. Hydrol. 543. (2016). Petry, J., Soulsby, C., Malcolm, I. A. & Youngson, A. F. Hydrological controls on nutrient concentrations and fluxes in agricultural catchments. Sci. Total Environ. 294 , 95–110 (2002). Song, C. et al. Impacts of natural wetland degradation on dissolved carbon dynamics in the Sanjiang Plain, Northeastern China. J. Hydrol. 398 , 26–32 (2011). Song, X. et al. Rainfall driven transport of carbon and nitrogen along karst slopes and associative interaction characteristic. J. Hydrol. 573 , 246–254 (2019). Tang, J. et al. Rainfall and Tillage Impacts on Soil Erosion of Sloping Cropland with Subtropical Monsoon Climate- A Case Study in Hilly Purple Soil area, China. J. Mt. Sci. 12 (1), 134–144 (2015). Wang, G., Chen, X., Cui, Z., Yue, S. & Zhang, F. Estimated reactive nitrogen losses for intensive maize production in China. Agric. Ecosyst. Environ. 197 , 293–300 (2014). Wang, L., Huang, X., Fang, N., Niu, Y. & Shi, Z. Selective transport of soil organic and inorganic carbon in eroded sediment in response to raindrop sizes and inflow rates in rainstorms. J. Hydrol. 575 , 42–53 (2019a). Wang, S., Feng, X., Wang, Y., Zheng, Z. & Lin, C. Characteristics of nitrogen loss in sloping farmland with purple soil in southwestern china during maize ( Zea mays L.) growth stages. Catena . 182 , 104169 (2019b). Wang, T. & Zhu, B. Nitrate loss via overland flow and interflow from a sloped farmland in the hilly area of purple soil, China. Nutr. Cycl. Agroecosyst . 90 , 309–319 (2011). Wu, L., Peng, M., Qiao, S. & Ma, X. Assessing impacts of rainfall intensity and slope on dissolved and adsorbed nitrogen loss under bare loessial soil by simulated rainfalls. Catena . 170 , 51–63 (2018). Xin, W. et al. Simulation Study of characteristics of runoff and sediment yield in the hill area with purple soils. Bull. Soil. Water Conserv. 28 (2), 31–35 (2008). (In Chinese). Xing, W., Yang, P., Ren, S., Chang, A. & Gao, W. Slope length effects on processes of total nitrogen loss under simulated rainfall. Catena . 139 , 73–81 (2016). Xu, X., Zheng, F., Qin, C., Wu, H. & Wilson, G. V. Impact of cornstalk buffer strip on hillslope soil erosion and its hydrodynamic understanding. Catena . 149 , 417–425 (2017). Yu, X., Xiao, S., Zhang, X. M., Fang, J. & H Characteristics of migration of dissolved organic carbon in overland flow and interflow in red soil sloping field. Res. Soil. Water Conserv. 27 (4), 16–22 (2020). (In Chinese). Yue, Y. et al. Lateral transport of soil carbon and land-atmosphere CO 2 flux induced by water erosion in China. Proc. Natl. Acad. Sci. 113, 6617–6622 (2016). 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. 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Introduction","content":"\u003cp\u003eSoil organic carbon (SOC) plays an essential role in maintaining soil quality, land ecosystem services, and the carbon cycle (Lal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dawson et al., 2007; Doetterl et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil erosion, especially water erosion, can lead to soil loss but can also affect dynamic changes of SOC (Lal and Pimentel et al., 2008; Berhe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), resulting in declining land productivity, increasing nonpoint-source pollution, and global warming (Kindler et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Agata et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The International Carbon Cycle Planning Organization (IPCC) estimated that globally, soil erosion has caused the redistribution of approximately 75\u0026nbsp;billion tons of soil and loss of 1\u0026ndash;5\u0026nbsp;billion tons of SOC each year (Yue et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous studies have mostly considered the dynamic changes of particulate organic carbon (POC) associated with sediment (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e), but dissolved organic carbon (DOC) associated with runoff is also an indispensable component of global carbon budgets (Lal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, previous study of soil DOC dynamics mainly focused on migration driven by surface flow (Mailapalli et al., 2012; Gaelen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lei et al., 2018; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, for well-developed interflow soils, the hydrological pathway of DOC loss via interflow cannot be ignored in evaluating DOC loss fluxes (Hua et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, studies are needed to quantify the DOC loss from both surface flow and interflow.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that the DOC loss fluxes via interflow account for a large proportion of the total DOC loss in runoff. For example, Li et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) identified that interflow as a crucial route for lateral DOC transport in the rainy season in the sloping land of Sichuan Purplish Soil region, and the annual loss fluxes of DOC by interflow for three years were 565.5, 802.1, and 1090.1 mg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. Hua et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) conducted a free-drain lysimeter experiment of Regosols in Southwest China, and found an annual loss flux of DOC through interflow of 865.5 mg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, which was 92 and five times higher than DOC loss fluxes through sediment and surface flow, respectively. Many factors affect the characteristics of soil DOC loss, including rainfall intensity, slope gradient, soil type, soil properties and agricultural land management (Jin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied four typical loess soils and found that sediment and surface flow are the main carriers of SOC, accounting for SOC losses of up to 90%. However, Wang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) and Fu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported that interflow, rather than surface flow, could be the main pathway of nutrient loss for purplish soil and weathered granite slopes. Gaelen et al. (2104) identified rainfall intensity as the main factor controlling DOC loss with runoff because it is the main driving force of runoff. Ma et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that rainfall intensity had a bigger effect on DOC lateral transport than on DOC vertical mobilization, and the DOC loss increased with increased intensity rainfall in the red soil region of Hunan province in China. Slope gradient also exerts strong effects on DOC loss (Mart\u0026iacute;nez-Mena et al., 2102). Fei et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) carried out artificially simulated rainfall experiments for red soils, finding that the total organic carbon loss of surface flow first increased and then decreased with increasing slope gradients. However, there was a continued rising trend as slope gradients increased in interflow. Wang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) found that nitrogen loss in the surface runoff and sediment yield increased as the slope gradients increased, and found the opposite trend regarding nitrogen loss in interflow.\u003c/p\u003e \u003cp\u003ePurplish soil, covers a total area of 1.6\u0026times;105 km\u003csup\u003e2\u003c/sup\u003e in the Sichuan Basin, in southwestern China, and is one of the most important soil types for agricultural production in the upper reaches of the Yangtze River (Wang and Zhu et al., 2011; Bah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Purplish soil is classified as Eutric Regosols (according to FAO Soil Taxonomy) and is developed from the products of the rapid weathering of underlying sandstones, siltstones, and mudstones. This weathered soil is characterized by a thin soil layer, low organic carbon, and high soil erodibility. Due to the relative thin soil layer and typical \u0026ldquo;soil-bedrock\u0026rdquo; dual structure, the soil layers can be easily saturated by rainfall (Jin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These soils also have high permeability, with extensive interflow through the \u0026ldquo;soil-bedrock\u0026rdquo; interface during the rainy season (Wang and Zhu et al., 2011; Hua et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Most studies of Purplish soil were carried out in the field under natural rainfall conditions, limiting conclusions about the lateral transport of DOC via surface flow and interflow due to the many uncontrollable factors of natural rainfall. To more carefully probe the contributions of surface flow and interflow to DOC loss, we conducted rainfall simulation experiment indoors with controlled rainfall intensity and slope gradient. The objectives of this study were as follows: (1) to investigate the dynamic changes of runoff rate and DOC concentration; (2) to quantify the distribution of DOC loss via surface flow and interflow; (3) to discuss the influence mechanism of rainfall intensity and slope gradient on DOC loss.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Soil collection and preparation\u003c/h2\u003e \u003cp\u003eThe soil used in this study was collected from Liangshan Yi Autonomous Prefecture (26\u0026deg;33\u0026prime;42\u0026Prime;N, 102\u0026deg;27\u0026prime;53\u0026Prime;E), in the southwest of Sichuan Province, China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region is dominated by a typical subtropical monsoon climate (Tang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with an average annual temperature of 16.2\u0026deg;C, and mean annual precipitation of 1111.6 mm, with about 95% of precipitation concentrated in the summer rainy season (Bah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This region is primarily purplish soil, with 35\u0026thinsp;~\u0026thinsp;45 cm soil tillage layer thickness. Soil samples were collected every 10 cm from the surface to 40 cm deep, and soil bulk density was determined by the cutting ring method. The soils from different layers were packed separately and transported to the laboratory. To maintain the natural state of the soil, no sieving was used (Xu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We mixed the soil material of the same layer thoroughly, removed the coarse stones and organic debris, and then air-dried the tested soil to achieve a water moisture of 10%\u0026plusmn;1%. The basic physicochemical properties of the tested soil are displayed 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\u003eBasic physicochemical properties of the tested soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003cp\u003e(g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTK\u003c/p\u003e \u003cp\u003e(g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAN\u003c/p\u003e \u003cp\u003e(mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003cp\u003e(mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003cp\u003e(mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eParticle size (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e120.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eBD: Soil bulk density, SOC: soil organic carbon, TN: total nitrogen, TP: total phosphorus, TK: total potassium, AN: available nitrogen, AP: available phosphorus, AK: available potassium.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Soil flume and preparation\u003c/h2\u003e \u003cp\u003eA three-dimensional soil flume (2.00 \u0026times; 1.00 \u0026times; 0.50, length \u0026times; width \u0026times; height) was constructed of steel sheets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The slope gradient of the soil flume can be adjusted from 0\u0026ndash;30\u0026deg; by hydraulic devices. The bottom of the soil flume is sealed to simulate a natural impermeable layer of a \u0026ldquo;soil-bedrock\u0026rdquo; interface. A \u0026ldquo;V-shaped\u0026rdquo; trough installed at the downslope edge of the flume was used to collect the surface flow and sediment, and the outlet at the bottom of the soil flume was used to collect interflow. Before filling the soil flume, a 5-cm layer of thick gravel and then gauze were put on the bottom of the flume. Next, a 35-cm soil layer was put on top of the gravel layer. To ensure the uniformity of the soil layer, the soil applied in 5-cm increments, with a soil bulk density of 1.3 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. In total, seven successive 5-cm soil layers were added to the soil flume. During filling, the soil was compacted to reduce the side-wall effect. For each layer, the surface was smoothed and then roughened to allow the adjacent two layers to tightly combine. Additionaly, new soil was used for each rainfall simulation event, rather than keeping the same soil in the flume for a series of rainfall simulation events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Rainfall simulator\u003c/h2\u003e \u003cp\u003eTo make the raindrop size and the distribution of the simulated rainfall closely resemble that of natural rainfall, a down sprinkler artificial rainfall simulator system (NYJL-10, Nanjing Forestry University) was employed in this study. The rainfall simulator included twelve sets of rainfall shower heads (three nozzle per head), and rainfall was applied at a rainfall height of 6 m. By changing both nozzle size and water pressure, the available rainfall intensity could be adjusted from 10 to 240 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with uniformity of 88%. Prior to the rainfall simulation experiment, the rainfall intensities were calibrated using a gauge and four rainfall barrels distributed around the soil flume.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Laboratory rainfall simulation experiments\u003c/h2\u003e \u003cp\u003eThe rainfall simulation experiments were carried out using the soil flume and rainfall simulator in the Key Laboratory of Mountain Hazards and Surface Process, Chengdu City, China. According to the classification of farmland of slopes (Comprehensive Scientific Expedition, 1990), three typical slope gradients of 5 (S5), 15 (S15), and 25\u0026deg; (S25) were selected, representing slight, steep, and the steepest slopes, respectively. Three rainfall intensities of 60 (R60), 90 (R90), and 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (R120) were selected, all within the range of typical erosive rainstorms in the Sichuan Basin. A total of 27 simulated rainfalls were conducted, with three rainfall intensities and three slope gradients, with three replicates of each treatment.\u003c/p\u003e \u003cp\u003ePrior to performing the experiment, the soil flume was subjected to pre-rainfall with 30 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e intensity and 0\u0026deg; slope gradient until achieving continuous interflow. After pre-saturation, the soil flume was adjusted to the experimental slope. To prevent water evaporation, a plastic sheet was used to cover the soil flume and stand for 24 h.\u003c/p\u003e \u003cp\u003eDuring the rainfall simulation, the initial times of surface flow and interflow were first recorded. The surface flow and interflow samples were collected at the corresponding outlets every 2 min. The water samples were volumetrically measured, passed through a 0.65 \u0026micro;m filter membrane, and then stored at 4\u0026deg;C for subsequent DOC analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Laboratory analysis\u003c/h2\u003e \u003cp\u003eThe basic physicochemical properties of the tested soil, including soil bulk density; pH; soil organic carbon; total nitrogen, phosphorus, and potassium; available nitrogen, phosphorus, and potassium; and the distribution of particle size were all measured by China agrochemical analysis (Bao, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The dissolved organic carbon of water samples was determined using a TOC analyzer (Elementar Analysensysteme GrnbH, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data analysis\u003c/h2\u003e \u003cp\u003eThe total DOC loss fluxes during a rainfall simulation event were calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Q=\\frac{{\\sum\\limits_{{i=1}}^{n} {{\\text{2}} \\times {C_i}} \\times {R_i}}}{{{\\text{1000}}}}\\)\u003c/span\u003e \u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere Q is the total DOC loss flux of surface flow or interflow in one rainfall simulation, g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; C is the DOC concentration of surface flow or interflow, mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; R is the runoff rate of surface flow or interflow, L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; n is the number of water samples of surface flow or interflow in one rainfall simulation.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using the software package SPSS 20.0, and all the figures were prepared using Origin 9.0 software. Treatment differences were determined using a one-way analysis of variance (ANOVA) at the significance level of 0.05. Multiple linear regression was applied to investigate the relationships between DOC loss and rainfall intensity and slope gradient. Pearson's correlations were used to study the correlations among the measured variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Surface flow and interflow loss\u003c/h2\u003e \u003cp\u003eThe runoff initiation times of surface flow and interflow ranged from 0.85 to 4.47 min, and 2.16 to 3.70 min, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both rainfall intensity and slope gradient exhibited accelerating effects on surface flow generation. For R60-S5 and R60-S15, the runoff initiation times of interflow were earlier than that of the surface flow, but under R90 and R120, the runoff initiation times of interflow lagged behind that of the surface flow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of runoff under different rainfall intensities and slopes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall intensity\u003c/p\u003e \u003cp\u003e(mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope gradient(\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitiation time of surface flow(min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitiation time of interflow (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRunoff rate of surface flow\u003c/p\u003e \u003cp\u003e(L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRunoff rate of interflow\u003c/p\u003e \u003cp\u003e(L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: different lowercase letters indicate significant differences at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe surface flow rate gradually increased with the rainfall duration, and then became stable with slight fluctuations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The time required to reach steady surface flow rate differed with different rainfall intensities and slopes. For low rainfall intensity or relative gentle slope (R60-S5, R60-S15, and R90-S5), the surface flow rates stabilized within 30\u0026ndash;35 min, but for large rainfall intensity (R90 and R120) or steep slope gradient (S25), the surface flow rates sharply increased and stabilized within 10 minutes. The surface flow rates increased as the rainfall intensity increased. On average, the surface flow rates of R60 (0.27\u0026ndash;0.48 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were significantly lower than those of R90 (1.19\u0026ndash;1.50 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and R120 (1.62\u0026ndash;1.95 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no remarkable difference between R90 and R120 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the slope gradient had no significant impact on surface flow rate, although the surface flow rate increased with the increase of slope.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe interflow curves were single-peak curves, showing trends of increasing initially and then decreasing under R60-S5 and R90-S5; the interflow rate gradually increased with rainfall duration under other treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The interflow rates decreased with increased rainfall intensity, in the order of R60 (0.22\u0026ndash;0.61 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;R90 (0.01\u0026ndash;0.35 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;R120 (0.01\u0026ndash;0.02 L m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The slope gradient had little effect on surface flow rates under high rainfall intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. DOC concentration in surface flow and interflow\u003c/h2\u003e \u003cp\u003eThere are two pathways for DOC loss by runoff, namely, surface flow and interflow. Under identical rainfall intensities and slope gradients, the hydrological characteristics of surface flow and interflow are very different, with significant differences in DOC loss (Fei et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the DOC concentration curves of surface flow peaked early and decreased gradually with the rainfall duration. The DOC concentration of surface flow decreased with the increase of rainfall intensities and showed no difference for different rainfall intensities. The DOC concentrations of surface flow of R60, R90, and R120 ranged from 13.34 to 26.61 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, from 11.56 to 29.25 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and from 10.11 to 22.44 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The average DOC concentration of surface flow at the slope of 15\u0026deg; was always the highest, with 18.37 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 60 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 17.84 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 90 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 17.00 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe DOC concentrations and the DOC loss fluxes in the surface flow and interflow for different rainfall intensities and slope gradients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall intensity\u003c/p\u003e \u003cp\u003e(mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope gradient(\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOC concentration in surface flow(mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOC concentration in interflow(mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurface flow DOC loss fluxes(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterflow DOC loss fluxes(g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: different lowercase letters indicate significant difference at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe DOC concentration curves of interflow showed the opposite trends from the surface flow curves, and gradually increased with the rainfall duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The concentration ranges of R60, R90, and R120 were 17.29\u0026ndash;34.89 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 14.32\u0026ndash;39.93 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 16.75\u0026ndash;41.71 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the low slope of S5, the average interflow DOC concentration at 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was 27.46 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, significantly higher than that of R90. For the high rainfall intensity of R90 and R120, the slope gradient has a significant effect on the interflow DOC concentration. The DOC concentration of interflow increased when rainfall intensity increased, and the average DOC concentration of interflow at the slope of 25\u0026deg; was always the highest, with 29.56 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 90 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 32.73 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Distribution of DOC loss fluxes via surface flow and interflow\u003c/h2\u003e \u003cp\u003eThe DOC loss fluxes via surface flow for 60, 90, and 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were 3.77\u0026ndash;6.94 g, 17.51\u0026ndash;25.70 g, and 19.69\u0026ndash;26.94 g, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the corresponding DOC loss fluxes via interflow were 6.55\u0026ndash;13.73 g, 0.41\u0026ndash;8.11 g, and 0.42\u0026ndash;0.55 g, respectively. As the rainfall intensity and slope gradient increased, the DOC loss fluxes of surface flow increased. However, the opposite effect was seen for rainfall intensity and slope gradient on DOC loss fluxes of interflow. The highest DOC loss flux of the surface flow was 26.94 g for R120-S15 and smallest was 3.77 g for R60-S5. For the DOC loss fluxes of the interflow, the greatest was 13.73 g for R60-S5 and the smallest was 0.42 g for R120-S5. In addition, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the ratios of surface flow and interflow DOC loss fluxes to the total DOC loss fluxes. Rainfall intensity and slope gradient had significant effects on the DOC loss distribution. The ratios of interflow DOC loss fluxes to the total DOC loss fluxes gradually decreased with the increase of rainfall intensity and slope gradient. For R60, interflow DOC loss percentages were 78.4%, 54.2%, and 51.0% at 5\u0026deg;, 15\u0026deg;, and 25\u0026deg;, respectively, all \u0026gt;\u0026thinsp;50%. For R120, the interflow DOC loss percentages were only 2.1%, 2.0%, and 1.7% at 5\u0026deg;, 15\u0026deg;, and 25\u0026deg;, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Influence factor correlation coefficient for DOC loss in surface flow and interflow\u003c/h2\u003e \u003cp\u003eMultiple linear regression equations were developed among slope, rainfall intensity and DOC loss fluxes for surface flow and interflow (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Surface flow DOC loss fluxes were positively correlated with rainfall intensity and slope (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.787, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), however, interflow flow DOC loss fluxes were negatively correlated with rainfall intensity and slope (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.822, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The partial correlation coefficients of rainfall intensity and slope were 0.881 and 0.426 for surface flow, and the corresponding absolute values of the partial correlation coefficients for interflow were 0.883 and 0.718. This indicated that the rainfall intensity has a greater impact on the DOC loss fluxes for both surface flow and interflow. Pearson analysis was performed and the correlations among rainfall intensity, slope gradient, runoff volume, and DOC loss via surface flow and interflow are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For surface flow, rainfall intensity and runoff volume were significantly correlated with DOC loss, with the highest correlation coefficient between runoff volume and DOC loss, 0.966, but insignificant correlations were seen between slope gradient and DOC loss. For interflow, rainfall intensity and slope gradient were negatively correlated with DOC loss, and runoff volume was positively correlated with DOC loss, with a correlation coefficient of 0.995. Additionally, the correlation between the three factors and DOC loss via surface flow and interflow were in the order of runoff volume\u0026thinsp;\u0026gt;\u0026thinsp;rainfall intensity\u0026thinsp;\u0026gt;\u0026thinsp;slope gradient.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression equations for slope gradients (S), rainfall intensities, (I) and DOC loss in the surface flow and interflow\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRunoff type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegression equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePartial correlation coefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope(\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntensity(mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.232S\u0026thinsp;+\u0026thinsp;0.305I-13.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY=-0.248S-0.15I\u0026thinsp;+\u0026thinsp;21.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of slope, rainfall intensity and runoff with DOC loss in surface flow and interflow\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface flow DOC loss(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterflow DOC loss(g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope(\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.436*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall intensity(mm h-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.860**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.795*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRunoff volume(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.966**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e**Indicates significant correlation at 0.01, *Indicates significant correlation at 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Surface flow and interflow loss\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that rainfall intensity and slope gradient are the most important factors affecting surface flow and interflow (Fu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For the high rainfall intensity of R90 and R120 and steep slope gradient of S25, the surface flow rates sharply increased with the rainfall duration, and reached a steady state after a short-term increase. For R60-S5, R60-S15, and R90-S5, the surface flow rates slowly increased, which lasted a long-term, and reached a steady state until the end of rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results suggest that excess infiltration may be the main mechanism of surface flow under large rainfall intensity and steep slope gradient, but saturation may be the main cause of surface flow under low rainfall intensity and gentle slope gradient, results that are consistent with those of Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, there are different trends of interflow rate for different rainfall intensities and slope gradients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Petry et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) reported that interflow includes preferential flow and matrix flow. Thus, further work should investigate the respective contributions of these two kinds of flow. In addition, defining the initial runoff time of the purplish soil and its influencing factors should increase our understanding of the different mechanisms of runoff generation (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Deng et al., 2020) The initial time of surface flow increased with the increase of rainfall intensity and slope (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), but the change in the initial time of interflow was more complex. We concluded that rainfall intensity is the main factor affecting the initial time of interflow if the slope gradient is low. For a slope of 5\u0026deg;, the initial time of interflow is prolonged with increasing rainfall intensity. This finding is consistent with the results of Xin et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) who studied the purplish soil area in southwest China.\u003c/p\u003e \u003cp\u003eThe observed distribution of surface flow and interflow indicates that rainfall intensity and slope gradient seriously affect the hydrological characteristics of purplish soil slopes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the amount of interflow comprised a larger proportion of the total runoff volume under conditions of lower rainfall intensity and slope. Specifically, the interflow percentage under R60-S5 was the highest, 69.2%, and the interflow percentages decreased as the rainfall intensity and slope gradient increased, with 0.9\u0026ndash;69.2%, 0.13\u0026ndash;43.25%, and 0.7\u0026ndash;31.9% for R60, R90, and R120, respectively. Hua et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggested that this phenomenon can be explained by water infiltration. Ma\u0026iuml;ga-Yaleu et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) proposed that the greater the rainfall intensity, the stronger the impact of raindrops, so the splash of raindrops can cause the sediment particles to clog the topsoil and form a crust that is resistant to water infiltration. Thus, the interflow would be low under high intensity. Additionally, Wang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) and Morbidelli et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also reported that the infiltration rate decreased as the slope increased. However, the results differ from those of Fei et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e), who reported that the interflow comprised the majority of the total runoff volume at steeper slopes, while the surface runoff comprised most of the runoff for gentle slope gradients of red soil. These differences may reflect differences in the soil types due to varied physical and chemical properties of the soil (Deng et al., 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. DOC loss via surface flow and interflow\u003c/h2\u003e \u003cp\u003eNutrient loss through runoff is mainly affected by the amount of runoff volume and the solubility of nutrients in the runoff (Lei et al., 2018; Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the trends of DOC concentration in the surface flow and interflow for different rainfall intensities and slope gradients. Opposite trends of DOC concentration were observed for the surface flow and interflow, consistent with the finding of Wang et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) that nitrogen concentration in the surface flow decreased with rainfall duration, but increased in the interflow. For surface flow, a large amount of soil organic carbon in the topsoil can be eroded away with increased rainfall duration, resulting in a gradual decrease of DOC concentration in surface flow, called a \u0026ldquo;flush effect\u0026rdquo; (Hathaway et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For interflow, at the start of rainfall simulation, the interflow was dominated by preferential flow, with the macropores in the soil profile serving as channels of preferential flow lacking soil organic carbon (Cey et al., 2010). Then the contribution of matrix flow gradually increased with rainfall duration, and adequate contact time between the infiltration water and the soil increased the dissolution of soil organic carbon in the interflow.\u003c/p\u003e \u003cp\u003eNo significant differences were found in the surface flow-associated DOC concentration among the different rainfall simulations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting only small effects of rainfall intensity and slope gradient on the DOC concentration of surface flow (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The influence of the slope factor on the DOC concentration of the surface flow has been controversial (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li and Wang et al., 2016). In this study, the DOC concentration of surface flow was highest for a slope of 15\u0026deg;, 17.00-18.37 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. These results are similar to the findings by Wu et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) that the surface flow velocity increased with increased slope gradient, and the finding of an increase in the dissolution of soil nutrients. However, at a critical slope gradient, the erosion transport capacity would decrease with increased slope gradient. Fei et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) explained that this switch could be attributed to breakdown of soil aggregates. For a gentle slope, raindrops can damage small soil aggregates, releasing soluble organic matter to increase DOC concentration of runoff. With a steeper slope gradient, large aggregates with lower soil organic carbon content tend to move more, thereby decreasing the DOC concentration of the surface flow (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). No significant differences in interflow DOC concentrations were also observed for the different rainfall intensities. Overall, for both surface flow and interflow, the rainfall intensity had little effect on DOC concentration, consistent with the findings of Jin et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We also found that the effect of slope gradient on DOC concentration of interflow was only significant with a rainfall intensity of 120 mm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with the lowest interflow DOC concentration for the 15\u0026deg; slope. This is because a large proportion of the topsoil enriched in organic carbon is eroded away by surface flow, with dissolving and infiltration of only a small fraction of the soluble organic carbon into the soil, resulting in a low concentration of interflow.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the DOC loss fluxes of surface flow ranged from 3.77\u0026ndash;26.94 g, and those of interflow varied from 3.77\u0026ndash;26.94 g. For R60, the DOC loss fluxes of interflow were 78.4%, 54.2%, and 51.0% of total DOC loss at 5\u0026deg;, 15\u0026deg; and 25\u0026deg;, respectively.These results indicated that the interflow is a crucial route of DOC loss under low rainfall intensity and low slope gradient, so nutrient loss through this pathway is significant for in purplish soil area, a finding that is consistent with that of Hua et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, Kindler et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argued that the DOC loss caused by interflow was comparatively small. This is because that DOC transport associated with interflow is a complex hydrological process that is determined by the amount of DOC in soil, the adsorption and desorption processes of DOC, and the physical and chemical properties of the soil (Fujii et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hua et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Influence factor correlation coefficient for DOC loss in surface flow and interflow\u003c/h2\u003e \u003cp\u003eThe DOC loss fluxes can be influenced by many factors, including rainfall intensity, slope gradients, surface runoff, and interflow (Xing et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Pearson analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the influence factor correlation coefficients for DOC loss in surface flow and interflow were ranked as follows: runoff volume\u0026thinsp;\u0026gt;\u0026thinsp;rainfall intensity\u0026thinsp;\u0026gt;\u0026thinsp;slope. Additionally, runoff volume exhibited a positive linear correlation with corresponding DOC loss fluxes in the surface flow (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the interflow (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Thus, to control DOC loss via runoff, runoff volume should first be considered, followed by rainfall intensity and slope gradient. Previous studies showed that soil and water conservation practices, including contour ridges, straw mulching, and canopy interception of precipitation, can effectively reduce the amount of surface flow by promoting water infiltration (Mailapalli et al., 2012; Xu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, at the same time, the volume of interflow probably increased in well-developed interflow soils (Yu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Here, the DOC concentrations in the interflow were 1.35\u0026ndash;2.34 times higher than those of the surface flow (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting that reducing the amount of surface flow may increase the risk of DOC loss through interflow under unreasonable measures. Therefore, additional research is needed to investigate the effects of prevention and control practices to reduce nutrient loss in purplish soil area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eRainfall simulation experiments of purplish soil were conducted under three rainfall intensities (60, 90, and 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and three slope gradients (5, 15 and 25\u0026deg;) to investigate runoff and DOC loss processes. The results showed that increasing the rainfall intensity and slope significantly accelerated surface flow generation and increased the surface flow rates, but delayed interflow generation and decreased interflow rates. The DOC concentration in the surface flow decreased with rainfall duration, and the opposite trend was seen for DOC concentration in interflow. For both surface flow and interflow, the rainfall intensity and slope gradient had little effect on DOC concentration, while these two factors affect DOC loss fluxes mainly by affecting the runoff rate of surface flow and interflow. Thus, the effects of rainfall intensity and slope gradient on DOC loss fluxes via surface flow and interflow were similar to the effects on the runoff rate of surface flow and interflow, that is, DOC loss fluxes of surface flow increased with increased rainfall intensity and slope gradient, and DOC loss fluxes of interflow decreased. In addition, interflow is a major hydrological pathway of DOC loss under low rainfall intensity and gentle slope gradient, but surface flow is more important under high rainfall intensity and steep slope gradient. Overall, the results indicate the lateral transport of DOC via interflow should be further studied to control carbon loss and enhance carbon sequestration in purplish soil area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMethodology, data collection, data analysis, writing\u0026mdash;original draft preparation, L.X.; supervision, modification, D.Z.; supervision, J.L.All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe research reported in this manuscript is funded by the Key Project of China National Tobacco Coporation Sichuan (Grants No. SCYC201802) and CAS President\u0026rsquo;s International Fellowship Initiative (Grants No. 2020VBB0008).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgata, N. et al. 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Impact of cornstalk buffer strip on hillslope soil erosion and its hydrodynamic understanding. \u003cem\u003eCatena\u003c/em\u003e. \u003cb\u003e149\u003c/b\u003e, 417\u0026ndash;425 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, X., Xiao, S., Zhang, X. M., Fang, J. \u0026amp; H Characteristics of migration of dissolved organic carbon in overland flow and interflow in red soil sloping field. \u003cem\u003eRes. Soil. Water Conserv.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (4), 16\u0026ndash;22 (2020). (In Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue, Y. et al. Lateral transport of soil carbon and land-atmosphere CO\u003csub\u003e2\u003c/sub\u003e flux induced by water erosion in China. Proc. Natl. Acad. Sci. 113, 6617\u0026ndash;6622 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"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":"Hydrological pathway, Dissolved organic carbon, Rainfall intensity, Slope gradient, Simulated rainfall, Purplish soil","lastPublishedDoi":"10.21203/rs.3.rs-4821149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4821149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDissolved organic carbon (DOC) is an indispensable component of the global carbon cycle and potentially affects aquatic ecosystems. Previous research on runoff process and DOC loss mainly focused on surface flow, with few reports of the hydrological pathway of interflow or DOC loss via interflow. To address this deficiency, a series of rainfall simulations were conducted with three rainfall intensities of 60, 90, and 120 mm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (R60, R90, and R120) and three slope gradients of 5, 15, and 25\u0026deg; (S5, S15, and S25) of purplish soil. The initial time of surface flow was faster under high rainfall intensity and steep slope, and the initial time of interflow increased with increased rainfall intensity under gentle slope. In general, the surface flow rates increased first, and reached a steady state within 10\u0026ndash;35 min. The interflow curves were single-peak curves for R60-S5 and R90-S5, but exhibited a continued rising trend for other treatments. The interflow volume occupied 69.2% of the total runoff volume under R60-S5, and the percentages of interflow decreased as the rainfall intensity and slope increased. These results indicated that interflow was an important hydrological pathway in this purplish soil area. The DOC concentration of the surface flow decreased with rainfall duration, with opposite trend for DOC concentration of interflow. The DOC concentrations in the interflow were 1.35\u0026ndash;2.34 times higher than those in the surface flow. However, the rainfall intensity and slope had little effect on DOC concentrations in both surface flow and interflow. Furthermore, the DOC loss fluxes via surface flow and interflow were 3.77\u0026ndash;26.94 g and 0.41\u0026ndash;13.73 g, respectively, and the ratios of interflow DOC loss fluxes to the total DOC loss fluxes gradually decreased with the increase of rainfall intensity and slope. Under R60, DOC loss via interflow was the major DOC loss pathway, accounting for 51.0%-78.4% of the total DOC loss, whereas for R90 and R120, DOC loss via surface accounted for \u0026gt;\u0026thinsp;90%. Moreover, runoff volume was positively linearly correlated with the corresponding DOC loss fluxes in both the surface flow (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and interflow (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results contribute to our understanding of the relationship between carbon cycle and soil erosion, and provide a scientific basis to estimate the fluxes of DOC loss and controll carbon loss in the purplish soil area of China.\u003c/p\u003e","manuscriptTitle":"Assessing impacts of rainfall intensity and slope gradient on runoff process and dissolved organic carbon loss via surface flow and interflow under simulated rainfall","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 11:51:23","doi":"10.21203/rs.3.rs-4821149/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":"91e02f80-31c8-4376-9907-f206e2009437","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37555433,"name":"Earth and environmental sciences/Hydrology"},{"id":37555434,"name":"Earth and environmental sciences/Ecology/Biogeochemistry"},{"id":37555435,"name":"Earth and environmental sciences/Solid earth sciences/Hydrogeology"},{"id":37555436,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"}],"tags":[],"updatedAt":"2025-02-21T07:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-18 11:51:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4821149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4821149","identity":"rs-4821149","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}