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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >A statistical model for streambank erosion in the Northern Gulf of Mexico coastal plain
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A statistical model for streambank erosion in the Northern Gulf of Mexico coastal plain

机译:墨西哥北湾北湾溪岸侵蚀的统计模型

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摘要

Stream restoration practitioners often rely upon empirical models to quantify annual streambank erosion rates and identify streambank erosion hotspots. Such models are designed to be widely applicable by incorporating readily available field measurements, but they must be calibrated to each hydrophysiographic region and may not reflect the dominant streambank erosion processes in a given region. Here, we present statistical models for streambank erosion using physical and environmental data collected at 53 locations throughout the northern Gulf of Mexico coastal plain. The data include channel geometry, bank characteristics, precipitation, above-ground biomass density, and root density, the latter two surveyed using techniques introduced here. We developed a statistical model selection process using Akaike's Information Criterion (AIC) and repeated cross validation (CV). Models derived from the literature that were applied a priori were only weak predictors of erosion rate, but AIC-CV model selection identified 3 strong statistical models. The best model according to AIC showed a significant correlation to lateral streambank erosion rates (R-2 = 0.54) and included the five strongest covariates of our dataset (bank slope, biomass density, curvature index, BEHI, and understory cover). When volumetric erosion rate (m(2)/year) was predicted, the fit of this model increased (R-2 = 0.65). CV-based selection resulted in a more conservative model with the four strongest covariates and a lower fit (R-2 = 0.47). The similarity of the AIC and CV models indicates the stability of the two-tier model selection approach, and suggests It has utility for modeling phenomena with many potential variables. Our models also showcase the ability of our biomass survey to quantify root reinforcement of streambanks. Our approach incorporates measurements familiar to the stream restoration community and can be applied throughout the northern Gulf of Mexico coastal plain, a region characterized by low relief fluvial valleys, unconsolidated alluvium and meandering single thread sand bed channels. The approach, which is based on field observations and robust statistical modeling, offers an alternative for stream restoration practitioners to more traditional streambank erosion prediction methods that underperform in the region, and may have applicability elsewhere.
机译:流恢复从业者经常依靠经验模型来量化年度流禁区侵蚀率并识别StreamBank侵蚀热点。这种模型的设计通过结合易于获得的现场测量来广泛应用,但是必须校准它们的每个水文镜区域,并且可能不会反映给定区域中的显着流箱腐蚀过程。在这里,我们使用在墨西哥北部北部湾沿海平原的53个地点收集的物理和环境数据来为Streambank侵蚀的统计模型。数据包括沟道几何形状,银行特征,降水,地上的地面生物质密度和根密度,使用此处介绍的技术进行了调查。我们使用Akaike的信息标准(AIC)和重复的交叉验证(CV)开发了统计模型选择过程。源自应用的文献的模型只是侵蚀率的弱预测因子,但AIC-CV模型选择确定了3个强大的统计模型。根据AIC的最佳模型显示与横向流银行侵蚀率的显着相关性(R-2 = 0.54),包括我们数据集的五个最强的协变量(银行斜率,生物质密度,曲率指数,Behi和林覆盖)。当预测体积侵蚀率(M(2)/年)时,该模型的适合增加(R-2 = 0.65)。基于CV的选择产生了一种更保守的模型,具有四个最强的协变量和较低的配合(R-2 = 0.47)。 AIC和CV模型的相似性表示双层模型选择方法的稳定性,并建议它具有建模现象具有许多潜在变量的实用性。我们的模型还展示了我们的生物量调查量化StreamBanks的根强化能力。我们的方法纳入了流恢复界熟悉的测量,可以在整个墨西哥沿海平原北部的北部北部,一个由低浮雕河谷,未覆盖的激增和蜿蜒的单螺纹砂床渠道为特征的区域。该方法基于现场观测和稳健的统计建模,提供了流恢复从业者的替代方案,以更加传统的StreamBank侵蚀预测方法,在该地区执行以下,并且可以在其他地方进行适用性。

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