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Uncertainty in forest road hydrologic modeling and catchment scale assessment of forest road sediment yield.

机译:林道水文模型的不确定性和林道沉积物产量的流域规模评估。

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摘要

The goal of this study was to advance methods for assessment of forest road hydrologic response and sediment yield at a catchment scale. This research looked at the effect of soil depth estimation on the Distributive Hydrology Soil Vegetation Model (DHSVM), assessed the uncertainty and accuracy of hydrologic modeling of forest roads by DHSVM, and evaluated the use of road runoff and sediment sampling for catchment scale road sediment estimates. The influence of soil depth estimation on DHSVM varied by spatial scale and hydrologic process modeled. Soil depth measurement improved DHSVM simulated streamflow and road ditchflow for the rising limb of the hydrograph with no improvements during baseflow. For site specific or small scale modeling a deterministic soil depth model fit to field measurements was best. For larger scale simulations of streamflow mean soil depth provided as good or better estimates.;Considerable uncertainty in estimates of road hydrologic response was observed from DHSVM. DHSVM over predicted individual road discharges. As the spatial scale and temporal scale was increased the uncertainty in DHSVM results decreased. This suggests that model structures chosen for DHSVM would be better determined with internal catchment data, at smaller scales. The GLUE assessment showed that change detection analysis with DHSVM will be limited to sites or scales of the catchment that behavioral model structures can be identified. From this research it was determined that only the catchment scale simulations and a few individual road locations could be used for change detection.;The storm runoff volumes and peak flows from road ditchflow had linear relationships with storm sediment load. These relationships had to be developed by classes of road locations and types in an intensively managed forest due to variability in road design, hydrologic response, and road use. Sediment from roads estimated from field measurements used with SEDMODL2 or WARSEM provided substantially lower estimates than without field measured adjustments. The use of road runoff for sediment estimation provided even lower catchment scale sediment results. DHSVM simulated road runoff for sediment estimation provided catchment scale results similar to the sediment yield estimated from observed road runoff.
机译:这项研究的目的是在集水区范围内发展评估林道水文响应和沉积物产量的方法。这项研究探讨了土壤深度估计对分布式水文土壤植被模型(DHSVM)的影响,评估了DHSVM对森林公路水文模型的不确定性和准确性,并评估了径流和泥沙采样在集水规模道路泥沙中的应用估计。土壤深度估算对DHSVM的影响随空间规模和水文过程建模而变化。土壤深度测量改进了DHSVM模拟的水文测量仪上升段的水流和道路沟流,而在基流期间没有改善。对于特定地点或小规模建模,确定性土壤深度模型最适合现场测量。对于更大范围的水流模拟,平均土壤深度可以作为较好或更好的估计值。 DHSVM超过预测的单个道路流量。随着空间尺度和时间尺度的增加,DHSVM结果的不确定性降低。这表明,为DHSVM选择的模型结构可以通过较小规模的内部流域数据更好地确定。 GLUE评估表明,使用DHSVM进行的变化检测分析将限于可以识别行为模型结构的流域的地点或规模。从这项研究中可以确定,只有流域规模模拟和一些单独的道路位置可用于变化检测。暴雨径流量和道路沟渠流量的峰值流量与暴雨泥沙量具有线性关系。由于道路设计,水文响应和道路使用的可变性,必须在集约化管理森林中按道路位置和类型的类别来建立这些关系。 SEDMODL2或WARSEM使用现场测量估算的道路泥沙比没有现场测量调整的估算值要低得多。使用道路径流进行沉积物估算可提供更低的集水规模沉积物结果。 DHSVM模拟的道路径流用于泥沙估算,其汇水规模结果与根据观测到的道路径流估算的泥沙产量相似。

著录项

  • 作者

    Surfleet, Christopher G.;

  • 作者单位

    Oregon State University.;

  • 授予单位 Oregon State University.;
  • 学科 Hydrology.;Agriculture Forestry and Wildlife.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 275 p.
  • 总页数 275
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 水文科学(水界物理学);森林生物学;
  • 关键词

  • 入库时间 2022-08-17 11:39:28

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