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首页> 外文期刊>Earth Surface Processes and Landforms: The journal of the British Geomorphological Research Group >Using air photos to parameterize landscape predictors of channel wetted width
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Using air photos to parameterize landscape predictors of channel wetted width

机译：使用航空照片参数化通道湿润宽度的景观预测因子

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We investigated which landscape and climate-related data (including information on hydrological source of flow) were statistically significant predictors of channel wetted width (WW) across a sizeable (2200 km~2) region of the UK. This was conducted specifically when flow was less than mean daily flow (MDF) and where channels are in a near natural state. Orthorectified air photos at 25cm spatial resolution were used to measureWW, with the magnitude of the errors in these measurements quantified. We used flow information from local gauging stations to ensure that channels were below MDF for the days on which the air photos were captured. The root mean squared difference between the field and air photo measurements of WW (n = 28 sites) was small(0.14 m) in comparison to median WW (3.07 m). We created points along sections of channels visible in air photos and used a terrain model to create drainage catchments for these points and computed their catchment area (CA). We selected a subset of points (n = 472) and measured their WW from air photos, and computed landscape-related data for each of their catchments (mean slope, mean annual rainfall, land cover type, elevation) and also mean BFIHOST, a quantitative index relating to hydrological source of flow. We used a linear mixed model to predict WW by including the landscape data (including CA~(0.5)) as fixed effects, plus a spatial covariance function estimated by residual maximum likelihood to determine unbiased estimates of the predictors. There was no evidence for retaining the spatial covariance function. With the exception of land cover, all the predictors were statistically significant and accounted for 76% of the variance of WW. When CA~(0.5) alone was used as a predictor it captured 54% of the variance. The vast majority of this difference was due to inclusion of an interaction between CA and hydrological source of flow (BFIHOST). As catchment area increases, those channels with larger mean catchment BFIHOST values (greater proportion of baseflow contribution) have narrower WW in comparison with those having smaller mean BFIHOST for the same CA. Improved predictions of channelWW(based on our findings) could be used in channel restoration.
机译：我们调查了哪些景观和气候相关数据（包括有关水文流量的信息）是英国较大面积（2200 km〜2）区域内通道湿润宽度（WW）的统计显着预测指标。具体是在流量小于平均每日流量（MDF）且通道处于接近自然状态时进行的。使用25cm空间分辨率的矫正空气照片测量WW，并量化这些测量中的误差幅度。我们使用了来自本地测量站的流量信息，以确保在拍摄空中照片的当天，通道位于MDF之下。与中位WW（3.07 m）相比，WW（n = 28个站点）的野外和空中照片测量之间的均方根差很小（0.14 m）。我们沿着空中照片中可见的通道部分创建了点，并使用地形模型为这些点创建了排水汇水面积，并计算了它们的汇水面积（CA）。我们选择了一个点子集（n = 472），并从空中照片测量了它们的WW，并计算了每个集水区的景观相关数据（平均坡度，平均年降雨量，土地覆盖类型，海拔）以及BFIHOST，与水文流量有关的定量指标。我们使用线性混合模型通过将景观数据（包括CA〜（0.5））作为固定效应来预测WW，再加上通过残差最大似然估计的空间协方差函数来确定预测变量的无偏估计。没有证据可以保留空间协方差函数。除土地覆盖率外，所有预测指标均具有统计学意义，占WW变异的76％。当单独使用CA〜（0.5）作为预测指标时，它捕获了54％的方差。这种差异的绝大部分归因于CA与水文流量源（BFIHOST）之间的相互作用。随着汇水面积的增加，与相同CA的平均BFIHOST值较小的通道相比，平均汇水BFIHOST值较大的通道（基流贡献比例较大）的WW较窄。 channelWW的改进预测（基于我们的发现）可用于信道恢复。

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