Prediction of lake depth across a 17-state region in the United States
Prediction of lake depth across a 17-state region in the United States
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DOI：
10.5268/iw-6.3.957
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发表时间：
2016-01-01
期刊：
影响因子：
3.1
通讯作者：
Stanley, Emily H.
中科院分区：
文献类型：
--
作者：
Oliver, Samantha K.;Soranno, Patricia A.;Stanley, Emily H.
湖泊深度是了解许多湖泊过程的一个重要特征，但全球绝大多数湖泊的深度是未知的。我们的目标是开发一个模型，预测湖泊深度使用地图导出的湖泊和陆地地貌特征的指标。建立在以前的模型，使用当地地形来预测湖泊深度，我们假设，地形，湖泊形状，或沉积过程的区域差异可能会导致特定区域的湖泊深度和映射功能之间的关系。因此，我们使用了一种混合建模方法，其中包括区域特定的模型参数。我们使用拉各斯的湖泊和地图数据建立了模型，其中包括8164个具有最大深度（Z（max））观测值的湖泊。该模型被用来预测深度为所有湖泊>= 4公顷（n = 42 443）的研究范围。湖泊表面积和最大坡度在100米的缓冲区是最好的预测Z（最大值）。表面积和地形之间的相互作用发生在地方和区域尺度上;表面积在陡峭的地形中有更大的影响，因此嵌入陡峭地形的大型湖泊比平坦地形的湖泊深得多。尽管样本量大且包含区域变异性，但模型性能（R-2 = 0.29，RMSE = 7.1 m）与其他已发表的模型相似。然而，相对误差因区域而异，这突出表明了采用区域方法进行湖深建模的重要性。此外，我们还提供了美国已知最大的观测和预测湖深值集合。
Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Z(max)) observations. The model was used to predict depth for all lakes >= 4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Z(max). Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R-2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.