棕壤横垄土壤侵蚀特征及其影响因素
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摘要
坡面是侵蚀产沙的重要来源,农业耕作措施是影响坡面侵蚀产沙的主要人为因素。其中,等高垄作是世界范围内广泛采用的提高作物产量、控制水土流失的农业措施。在现有坡面土壤侵蚀模型中,RUSLE2模型对等高垄作措施考虑较为充分。模型中利用垄高、垄向坡度等因子对等高耕作的水土保持措施因子进行了估算。等高垄作系统中,垄沟内的雨水汇集可导致横垄垮塌和渗流发生,从而加剧土壤侵蚀。但由于观测资料的局限性,包括RUSLE2模型在内的相关研究成果较少涉及到垄面侵蚀、横垄垮塌及渗流条件下的横垄侵蚀过程,且在评价微地貌及垄沟几何结构等因素及其交互作用对横垄侵蚀的影响等方面尚有不足之处。
通过调查鲁中南山地丘陵区棕壤坡耕地等高垄作系统中的微地貌和垄沟几何结构参数,设计了新型实验土槽。该土槽可同时调节垄向坡度和坡面坡度,并可设定自由下渗和渗流2种不同的实验条件。在自由下渗条件下,利用正交设计对垄向坡度(RG)、坡面坡度(FS)、垄高(H)、垄宽(W)和雨强(RI)等5个因素2个水平进行了组合,共包括16个组合处理,研究了垄面侵蚀过程和横垄垮塌条件下侵蚀产沙特征及其影响因素；在渗流条件下,利用正交旋转回归组合设计对垄向坡度、坡面坡度、垄高等3个因素5个水平进行了组合,共23个处理,分析了各因素及其之间的交互作用对横垄渗流及侵蚀产沙的影响。取得的主要成果和结论如下：
(1)在横垄垮塌前,垄面土壤侵蚀过程可分为沟间侵蚀和细沟侵蚀两个阶段。该两个阶段的产流量分别占总产流量的44.2%和55.8%,细沟侵蚀阶段的产沙量占总产沙量的87.2%。沟间侵蚀阶段持续时间较细沟侵蚀阶段长,占总侵蚀阶段持续时间的72.3%。在沟间侵蚀阶段,垄宽和雨强对每分钟产流量具有显著正向作用(p<0.01),其贡献率分别为33.1%和28.7%。每分钟产沙量受到垄宽和雨强的正向显著作用,贡献率分别为14.8%和17.0%。垄高对每分钟产流量具有正向显著作用(16.6%),但是对产沙量的影响不显著。坡面坡度对每分钟产沙量具有正向显著作用(8.3%),但是对产流量影响不显著。每分钟产流量主要受到垄沟几何结构的影响,而产沙量则主要受微地貌因素的影响。在细沟侵蚀阶段,垄高对产流具有负向显著作用,其原因在于垄高的增大可蓄积更多的雨水、延长入渗时间,并可提高水头,从而增大雨水入渗。垄向坡度对产沙具有正向显著作用,其原因是垄向坡度的增大,可减小土壤颗粒间粘结力,降低坡稳性。因子之间的交互作用对垄面的产流产沙具有重要作用,如坡面坡度与雨强对两个阶段产流量的负向交互作用、降雨强度与垄宽对细沟侵蚀阶段产沙量的正向交互作用、垄向坡度与垄宽对沟间侵蚀阶段持续时间的正向交互作用。
(2)当有横垄垮塌发生时,除垄向坡度因子之外的其它因素对横垄产流产沙均具有显著影响(p<0.01)。降雨强度是影响产流量的最重要因素,其贡献率为68.1%,其次依次为垄高、坡面坡度和垄宽。坡面坡度与雨强的交互作用对产流量具有负向显著影响(贡献率为5.4%)。在低雨强下,径流量随着坡度的增大而增大,但是在高雨强下,径流量随坡度增大呈减小趋势。垄高与垄宽的负交互作用、坡面坡度与垄高的正交互作用对产流量的影响均达到显著水平。垄高对产沙量具有显著负向作用,其贡献率达到21.4%,超过了雨强的影响(贡献率为19.4%)。由此表明,相对于产流,通过调整微地貌因子和垄沟几何结构可对产沙起到更有效的控制作用。垄向坡度与垄宽、坡面坡度与垄高、雨强与垄高的交互作用对产沙量的正向作用均达到了显著水平,而坡面坡度与雨强的交互作用则为负向影响。根据影因子及其之间交互作用对产流产沙量影响的贡献率和正负性,确定了控制产流产沙的最佳因子组合。控制产流的因子组合分为两种情况：在较低雨强下,因子组合为RG1, FS1, H2和W2；在较高雨强下,因子组合为RG1, FS2, H2和W2。控制产沙的因子组合不受雨强大小的影响,均为RG1, FS1, H1和W2(下标1和2为实验采用因子的低水平与高水平)。
(3)渗流条件下,横垄土壤侵蚀过程可分为四个阶段：沟间侵蚀,溯源侵蚀,横垄崩塌和细沟侵蚀。人工模拟降雨过程中的渗流率相比供水渗流阶段的有所减少,其原因可能是降雨对土体的压实作用和分散的土壤颗粒对土壤空隙的堵塞作用。以垄向坡度、坡向坡度和垄高三个因子作为自变量,分别建立了产流量和产沙量的二次多项式回归模型,模型的决定系数分别为0.743和0.545。垄向坡度和坡向坡度对产沙量的影响相对径流较大,而垄高对产流量的影响较大,且随着垄高的增大,其影响的程度越大。随着因子水平的提高,3个因子对产流量的影响具有不同的趋势：垄高的影响趋于持续增大,垄向坡度的影响呈先降低后升高趋势,坡面坡度则为先升高后降低趋势。垄高、垄向坡度和坡向坡度对产沙量的影响随着因子水平的增大,呈先增大后减小的单峰趋势。依据产沙量变化曲线可以得到最大产沙量及其对应的因子水平。在达到最大产沙量之前,坡向坡度与其它两个因素相比,对产沙量的影响具较大的增加率；当达到最大产沙量后,坡向坡度则具有较快的减小率。垄向坡度、坡向坡度和垄高之间的交互作用对产流和产沙量的影响在p<0.1水平上均不显著。在实际中,避免采用最大产沙量所在的因子水平,可更好地发挥等高垄作措施控制土壤侵蚀的作用。
(4)渗流条件下的产沙量相对于自由入渗条件下,增加幅度可达到15倍。以垄向坡度、坡向坡度和垄高作为自变量,渗流率可用其二次多项式回归模型予以模拟,模型的决定系数为0.759,且达到显著水平(p<0.01)。渗流率主要受到垄向坡度及其二次项和垄高的二次项的影响,受这三个因素间交互作用的影响不显著。利用实测或预测渗流率作为输入因子,可提高侵蚀产沙的二次多项式回归模型的模拟精度：与未纳入渗流率的回归模型相比,决定系数(R2)分别从0.743提高至0.915和0.893,均方根误差(RMSE)从0.67分别减少至0.38和0.43。引入实测渗流率后的修正侵蚀产沙模型比引入预测渗流率的修正模型,具有更高的显著水平,其p值分别为0.007和0.016。引入实测渗流率后的修正产沙模型,更多的识别出了具有显著影响的因素和因子间的交互作用,如垄向坡度及其与坡面坡度、垄高和渗流率的交互作用；坡向坡度的二次项及其与垄高、渗流率的交互作用。引入预测渗流率的修正产沙模型中,只有渗流率的二次项、垄向坡度与渗流率的交互作用具有显著影响,因此通过删除不显著影响因素,其形式可被简化。简化的产沙量预测模型所需参数少、易于应用,特别是在渗流率无法获取时,可对产沙量进行较为准确的预测。
Field slope is one of the most important areas for sediment generation effected by tillage cultivation. Contour ridging, generally shaped as ridge and furrow, is an effective soil conservation practice for increasing crop yield used throughout the world. Among the existing soil erosion models, the soil conservation benefit for contour ridging has been considered to the greatest extent in the Revised Universal Soil Loss Equation, Version2(RUSLE2). In RUSLE2, the factors of rigde height and row grade is used for assess the benefit of contour ridging (Pc) as subfactors. The accumulation of rainwater in the ridge and furrow system may couse ridge collapse and seepage generation, which could increase soil erosion. Laking of sufficient observation data, the soil erosion process on row siderslopes, erosion induced by ridge failure and erosion characteristic under seepage conditions in contour ridging systems has not been carefully considered in RUSLE2model, and the effect of factors (e.g. microtopography, ridge geometry) and their interactions on soil erosion is not quantitatively interpreted and need further studies.
Through field investigation of slope land microtopography and ridge geometry in the hilly and mountainous areas of central and southern Shandong province, a new type of experimental plot was designing to imitate microtopographic relief of ridge and furrow system. In such plot, the row grade and field slope can be changed simultaneously and seepage conditions can be created. In this study,32rainfall simulation experiments were performed in drainage conditions to analyze the effects and interaction of two ridge geometry indices (ridge width and ridge height), two microtopography indices (field slope and row grade), and rainfall intensity on soil erosion with two replications. To address the importance of seepage in soil erosion, a total of23treatments with3factors (e.g., ridge height, row grade and field slope) in5levels were arranged in an orthogonal rotatable central composite design. To predict the sediment yield and evaluate the significance of the effects and interactions of these factors, second-order polynomial regression models were built and the regression coefficients were tested. The main results and conditions were listed as bellows:
(1) Before contour failure, soil erosion process on the row sideslope could be classified as interrill erosion period and rill erosion period.The runoff generated during the two periods accounted for about44.2%and55.8%of the total runoff, respectively. Sediment yield in the rill erosion period was the main source for the entire sediment with the contribution of87.2%. The duration for the interrill erosion period was longer than that of rill erosion period and occupied72.3%of the entire duration. In the interrill period, the runoff and sediment yield per min were positively affected by ridge width and rainfall intensity, with the contributions of33.1%and28.7%for runoff and14.8%and17.0%for sediment yield, respectively. Ridge height had significant and positive effect on runoff per min but not on sediment yield per min. runoff per min was mainly influenced by ridge geometry factors, while the sediment yield per min mainly by the microtopography relief. During the rill erosion period, runoff per min was significantly and negatively affected by ridge height with the reason that higher ridge height could retain more rain water for a longer time under a higher water head to lead more water infiltration. Through reducing the soil cohesiveness and slope stability, a greater row grade could significantly increase the sediment yield per min. The interactions between some factors played an important role in the soil erosion on row sideslopes, e.g., the negative interaction between field slope and rainfall intensity on runoff during both periods, the positive interaction between rainfall intensity and ridge width on sediment yield in the rill erosion period, the positive interaction between row grade and ridge width on the duration of the rill erosion periods.
(2) When the contour failure occurred during the erosion process in ridge system, except for row grade, all of the factors in this study had significant effect on runoff and sediment yield at p<0.01. The runoff mainly affected by rainfall intensity with the highest contribution of68.1%, and then followed by the factor of ridge height, field slope, and ridge width. The interaction between field slope and rainfall intensity significantly and negatively affected runoff with a contribution of5.4%. under a lower rainfall intensity, the runoff showed a increasing trend with the field slope increasing, while under a higher rainfall intensity, the the runoff showed a decreasing trend. Some interactions also exerted significant effect on runoff, e.g., the negative interaction between ridge height and width and the positive interaction between field slope and ridge height. Ridge height, with a negative effect, had a greater influence on sediment yield than rainfall intensity with the contribution of21.4%and19.4, indicating that adjusting microtopography relief and ridge geometry may have a better controlling benefit on sediment yield than on runoff. Additionally, the effect of row grade and its interaction with ridge width on sediment yield were positive and significant. According to the contribution of the effect and interactions, the optimal combinations of factors for runoff and sediment controlling were determined. To control runoff, the optimal combinations were FS1, H2, and W2under lower rainfall intensity, and RG1, FS2, H2, and W2under higher rainfall intensity. The optimal combinations for sediment controlling were RG1, FS1, H1, and W2. Here, the subscripts1and2represented the lower and higher factor level, respectively.
(3) Under seepage conditions, soil erosion on row sideslope could be classified as four period:interrill erosion, headward erosion, contour failure, and rill erosion. Compared with the water supplying conditions, seepage dischare became smaller during the simulated rainfall conditions probably caused by rainfall pressure on soil matix and splashed soil partical clogging soil porosity. Taking row grade, field slope and ridge height as input variable, the second-order polynomial regression models for runoff and sediment yield were built, with the determination coefficient R2-0.743and0.545, respectively. Compared to runoff, the effect of row grade and field slope was greater on sediment yield. Ridge height had a greater influence on runoff than sediment yield with an increasing positive effect. The impact of row grade, ridge height, and field slope on sediment yield showed as a convex curve with factor level increasing. From the convex curve for each factor, the maximum sediment yield could be calculated out and the monofactor level where the maximum sediment yield occurred could be determined accordingly. Compared to the other two factors, field slope presented a greater increasing impact on sediment yield before the maximum sediment yield occurred, and after that field slope exerted a greater decreasing effect. Even at p<0.1, the interactions between the field slope, ridge height, and row grade had no significantly effect on both runoff and sediment yield. The results indicated that avoiding the factor level where the maximum sediment yield occurred, could better use contour tillage to control soil erosion.
(4) Sediment yield under seepage conditions was as higher as about15times than under drainage conditions. Seepage disarge could be estimated using the second-order polynomial regression models with row grade, field slope and ridge height as input parameters. The dermination coefficient of the seepage discharge estimation model is0.759with significance at p<0.01.The seepage discharge were mainly affected by row grade, quadratic terms of row grade and ridge height. The interaction between these factors had no significant effect on seepage discharge. Using the measured or predicted seepage discharge as an input variable, the coefficient of determination (R2) increased from0.743to0.915or0.893and the root-mean square error (RMSE) decreased from0.67to0.38or0.43, respectively. The impoved sediment yield regression model combined with measured seepage discharge showed a greater significance than that combined with predicted seepage discharge, and the p value was0.007and0.016, respectively. With measured seepage discharge combined, the regression model presented more significanct effect and interactions, e.g., the row grade and the interaction between row grade and ridge height, field slope, and seepage discharge. The quadratic terms of field slope and the interaction between field slope and row grade and seepage discharge were also detected as significant items. With predicted seepage discharge combined, the regression model only included two significant items, i.e., quadratic terms of seepage discharge and the interaction between row grade and seepage discharge. Therefore, through removing the non-significant items, the predicted seepage discharge combined regression model could be simplified to a concierge form that could be easily used, especially when the seepage discharge could not be measured.
通过调查鲁中南山地丘陵区棕壤坡耕地等高垄作系统中的微地貌和垄沟几何结构参数,设计了新型实验土槽。该土槽可同时调节垄向坡度和坡面坡度,并可设定自由下渗和渗流2种不同的实验条件。在自由下渗条件下,利用正交设计对垄向坡度(RG)、坡面坡度(FS)、垄高(H)、垄宽(W)和雨强(RI)等5个因素2个水平进行了组合,共包括16个组合处理,研究了垄面侵蚀过程和横垄垮塌条件下侵蚀产沙特征及其影响因素；在渗流条件下,利用正交旋转回归组合设计对垄向坡度、坡面坡度、垄高等3个因素5个水平进行了组合,共23个处理,分析了各因素及其之间的交互作用对横垄渗流及侵蚀产沙的影响。取得的主要成果和结论如下：
(1)在横垄垮塌前,垄面土壤侵蚀过程可分为沟间侵蚀和细沟侵蚀两个阶段。该两个阶段的产流量分别占总产流量的44.2%和55.8%,细沟侵蚀阶段的产沙量占总产沙量的87.2%。沟间侵蚀阶段持续时间较细沟侵蚀阶段长,占总侵蚀阶段持续时间的72.3%。在沟间侵蚀阶段,垄宽和雨强对每分钟产流量具有显著正向作用(p<0.01),其贡献率分别为33.1%和28.7%。每分钟产沙量受到垄宽和雨强的正向显著作用,贡献率分别为14.8%和17.0%。垄高对每分钟产流量具有正向显著作用(16.6%),但是对产沙量的影响不显著。坡面坡度对每分钟产沙量具有正向显著作用(8.3%),但是对产流量影响不显著。每分钟产流量主要受到垄沟几何结构的影响,而产沙量则主要受微地貌因素的影响。在细沟侵蚀阶段,垄高对产流具有负向显著作用,其原因在于垄高的增大可蓄积更多的雨水、延长入渗时间,并可提高水头,从而增大雨水入渗。垄向坡度对产沙具有正向显著作用,其原因是垄向坡度的增大,可减小土壤颗粒间粘结力,降低坡稳性。因子之间的交互作用对垄面的产流产沙具有重要作用,如坡面坡度与雨强对两个阶段产流量的负向交互作用、降雨强度与垄宽对细沟侵蚀阶段产沙量的正向交互作用、垄向坡度与垄宽对沟间侵蚀阶段持续时间的正向交互作用。
(2)当有横垄垮塌发生时,除垄向坡度因子之外的其它因素对横垄产流产沙均具有显著影响(p<0.01)。降雨强度是影响产流量的最重要因素,其贡献率为68.1%,其次依次为垄高、坡面坡度和垄宽。坡面坡度与雨强的交互作用对产流量具有负向显著影响(贡献率为5.4%)。在低雨强下,径流量随着坡度的增大而增大,但是在高雨强下,径流量随坡度增大呈减小趋势。垄高与垄宽的负交互作用、坡面坡度与垄高的正交互作用对产流量的影响均达到显著水平。垄高对产沙量具有显著负向作用,其贡献率达到21.4%,超过了雨强的影响(贡献率为19.4%)。由此表明,相对于产流,通过调整微地貌因子和垄沟几何结构可对产沙起到更有效的控制作用。垄向坡度与垄宽、坡面坡度与垄高、雨强与垄高的交互作用对产沙量的正向作用均达到了显著水平,而坡面坡度与雨强的交互作用则为负向影响。根据影因子及其之间交互作用对产流产沙量影响的贡献率和正负性,确定了控制产流产沙的最佳因子组合。控制产流的因子组合分为两种情况：在较低雨强下,因子组合为RG1, FS1, H2和W2；在较高雨强下,因子组合为RG1, FS2, H2和W2。控制产沙的因子组合不受雨强大小的影响,均为RG1, FS1, H1和W2(下标1和2为实验采用因子的低水平与高水平)。
(3)渗流条件下,横垄土壤侵蚀过程可分为四个阶段：沟间侵蚀,溯源侵蚀,横垄崩塌和细沟侵蚀。人工模拟降雨过程中的渗流率相比供水渗流阶段的有所减少,其原因可能是降雨对土体的压实作用和分散的土壤颗粒对土壤空隙的堵塞作用。以垄向坡度、坡向坡度和垄高三个因子作为自变量,分别建立了产流量和产沙量的二次多项式回归模型,模型的决定系数分别为0.743和0.545。垄向坡度和坡向坡度对产沙量的影响相对径流较大,而垄高对产流量的影响较大,且随着垄高的增大,其影响的程度越大。随着因子水平的提高,3个因子对产流量的影响具有不同的趋势：垄高的影响趋于持续增大,垄向坡度的影响呈先降低后升高趋势,坡面坡度则为先升高后降低趋势。垄高、垄向坡度和坡向坡度对产沙量的影响随着因子水平的增大,呈先增大后减小的单峰趋势。依据产沙量变化曲线可以得到最大产沙量及其对应的因子水平。在达到最大产沙量之前,坡向坡度与其它两个因素相比,对产沙量的影响具较大的增加率；当达到最大产沙量后,坡向坡度则具有较快的减小率。垄向坡度、坡向坡度和垄高之间的交互作用对产流和产沙量的影响在p<0.1水平上均不显著。在实际中,避免采用最大产沙量所在的因子水平,可更好地发挥等高垄作措施控制土壤侵蚀的作用。
(4)渗流条件下的产沙量相对于自由入渗条件下,增加幅度可达到15倍。以垄向坡度、坡向坡度和垄高作为自变量,渗流率可用其二次多项式回归模型予以模拟,模型的决定系数为0.759,且达到显著水平(p<0.01)。渗流率主要受到垄向坡度及其二次项和垄高的二次项的影响,受这三个因素间交互作用的影响不显著。利用实测或预测渗流率作为输入因子,可提高侵蚀产沙的二次多项式回归模型的模拟精度：与未纳入渗流率的回归模型相比,决定系数(R2)分别从0.743提高至0.915和0.893,均方根误差(RMSE)从0.67分别减少至0.38和0.43。引入实测渗流率后的修正侵蚀产沙模型比引入预测渗流率的修正模型,具有更高的显著水平,其p值分别为0.007和0.016。引入实测渗流率后的修正产沙模型,更多的识别出了具有显著影响的因素和因子间的交互作用,如垄向坡度及其与坡面坡度、垄高和渗流率的交互作用；坡向坡度的二次项及其与垄高、渗流率的交互作用。引入预测渗流率的修正产沙模型中,只有渗流率的二次项、垄向坡度与渗流率的交互作用具有显著影响,因此通过删除不显著影响因素,其形式可被简化。简化的产沙量预测模型所需参数少、易于应用,特别是在渗流率无法获取时,可对产沙量进行较为准确的预测。
Field slope is one of the most important areas for sediment generation effected by tillage cultivation. Contour ridging, generally shaped as ridge and furrow, is an effective soil conservation practice for increasing crop yield used throughout the world. Among the existing soil erosion models, the soil conservation benefit for contour ridging has been considered to the greatest extent in the Revised Universal Soil Loss Equation, Version2(RUSLE2). In RUSLE2, the factors of rigde height and row grade is used for assess the benefit of contour ridging (Pc) as subfactors. The accumulation of rainwater in the ridge and furrow system may couse ridge collapse and seepage generation, which could increase soil erosion. Laking of sufficient observation data, the soil erosion process on row siderslopes, erosion induced by ridge failure and erosion characteristic under seepage conditions in contour ridging systems has not been carefully considered in RUSLE2model, and the effect of factors (e.g. microtopography, ridge geometry) and their interactions on soil erosion is not quantitatively interpreted and need further studies.
Through field investigation of slope land microtopography and ridge geometry in the hilly and mountainous areas of central and southern Shandong province, a new type of experimental plot was designing to imitate microtopographic relief of ridge and furrow system. In such plot, the row grade and field slope can be changed simultaneously and seepage conditions can be created. In this study,32rainfall simulation experiments were performed in drainage conditions to analyze the effects and interaction of two ridge geometry indices (ridge width and ridge height), two microtopography indices (field slope and row grade), and rainfall intensity on soil erosion with two replications. To address the importance of seepage in soil erosion, a total of23treatments with3factors (e.g., ridge height, row grade and field slope) in5levels were arranged in an orthogonal rotatable central composite design. To predict the sediment yield and evaluate the significance of the effects and interactions of these factors, second-order polynomial regression models were built and the regression coefficients were tested. The main results and conditions were listed as bellows:
(1) Before contour failure, soil erosion process on the row sideslope could be classified as interrill erosion period and rill erosion period.The runoff generated during the two periods accounted for about44.2%and55.8%of the total runoff, respectively. Sediment yield in the rill erosion period was the main source for the entire sediment with the contribution of87.2%. The duration for the interrill erosion period was longer than that of rill erosion period and occupied72.3%of the entire duration. In the interrill period, the runoff and sediment yield per min were positively affected by ridge width and rainfall intensity, with the contributions of33.1%and28.7%for runoff and14.8%and17.0%for sediment yield, respectively. Ridge height had significant and positive effect on runoff per min but not on sediment yield per min. runoff per min was mainly influenced by ridge geometry factors, while the sediment yield per min mainly by the microtopography relief. During the rill erosion period, runoff per min was significantly and negatively affected by ridge height with the reason that higher ridge height could retain more rain water for a longer time under a higher water head to lead more water infiltration. Through reducing the soil cohesiveness and slope stability, a greater row grade could significantly increase the sediment yield per min. The interactions between some factors played an important role in the soil erosion on row sideslopes, e.g., the negative interaction between field slope and rainfall intensity on runoff during both periods, the positive interaction between rainfall intensity and ridge width on sediment yield in the rill erosion period, the positive interaction between row grade and ridge width on the duration of the rill erosion periods.
(2) When the contour failure occurred during the erosion process in ridge system, except for row grade, all of the factors in this study had significant effect on runoff and sediment yield at p<0.01. The runoff mainly affected by rainfall intensity with the highest contribution of68.1%, and then followed by the factor of ridge height, field slope, and ridge width. The interaction between field slope and rainfall intensity significantly and negatively affected runoff with a contribution of5.4%. under a lower rainfall intensity, the runoff showed a increasing trend with the field slope increasing, while under a higher rainfall intensity, the the runoff showed a decreasing trend. Some interactions also exerted significant effect on runoff, e.g., the negative interaction between ridge height and width and the positive interaction between field slope and ridge height. Ridge height, with a negative effect, had a greater influence on sediment yield than rainfall intensity with the contribution of21.4%and19.4, indicating that adjusting microtopography relief and ridge geometry may have a better controlling benefit on sediment yield than on runoff. Additionally, the effect of row grade and its interaction with ridge width on sediment yield were positive and significant. According to the contribution of the effect and interactions, the optimal combinations of factors for runoff and sediment controlling were determined. To control runoff, the optimal combinations were FS1, H2, and W2under lower rainfall intensity, and RG1, FS2, H2, and W2under higher rainfall intensity. The optimal combinations for sediment controlling were RG1, FS1, H1, and W2. Here, the subscripts1and2represented the lower and higher factor level, respectively.
(3) Under seepage conditions, soil erosion on row sideslope could be classified as four period:interrill erosion, headward erosion, contour failure, and rill erosion. Compared with the water supplying conditions, seepage dischare became smaller during the simulated rainfall conditions probably caused by rainfall pressure on soil matix and splashed soil partical clogging soil porosity. Taking row grade, field slope and ridge height as input variable, the second-order polynomial regression models for runoff and sediment yield were built, with the determination coefficient R2-0.743and0.545, respectively. Compared to runoff, the effect of row grade and field slope was greater on sediment yield. Ridge height had a greater influence on runoff than sediment yield with an increasing positive effect. The impact of row grade, ridge height, and field slope on sediment yield showed as a convex curve with factor level increasing. From the convex curve for each factor, the maximum sediment yield could be calculated out and the monofactor level where the maximum sediment yield occurred could be determined accordingly. Compared to the other two factors, field slope presented a greater increasing impact on sediment yield before the maximum sediment yield occurred, and after that field slope exerted a greater decreasing effect. Even at p<0.1, the interactions between the field slope, ridge height, and row grade had no significantly effect on both runoff and sediment yield. The results indicated that avoiding the factor level where the maximum sediment yield occurred, could better use contour tillage to control soil erosion.
(4) Sediment yield under seepage conditions was as higher as about15times than under drainage conditions. Seepage disarge could be estimated using the second-order polynomial regression models with row grade, field slope and ridge height as input parameters. The dermination coefficient of the seepage discharge estimation model is0.759with significance at p<0.01.The seepage discharge were mainly affected by row grade, quadratic terms of row grade and ridge height. The interaction between these factors had no significant effect on seepage discharge. Using the measured or predicted seepage discharge as an input variable, the coefficient of determination (R2) increased from0.743to0.915or0.893and the root-mean square error (RMSE) decreased from0.67to0.38or0.43, respectively. The impoved sediment yield regression model combined with measured seepage discharge showed a greater significance than that combined with predicted seepage discharge, and the p value was0.007and0.016, respectively. With measured seepage discharge combined, the regression model presented more significanct effect and interactions, e.g., the row grade and the interaction between row grade and ridge height, field slope, and seepage discharge. The quadratic terms of field slope and the interaction between field slope and row grade and seepage discharge were also detected as significant items. With predicted seepage discharge combined, the regression model only included two significant items, i.e., quadratic terms of seepage discharge and the interaction between row grade and seepage discharge. Therefore, through removing the non-significant items, the predicted seepage discharge combined regression model could be simplified to a concierge form that could be easily used, especially when the seepage discharge could not be measured.
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