Predicting eutrophication status in reservoirs at large spatial scales using landscape and morphometric variables
Predicting eutrophication status in reservoirs at large spatial scales using landscape and morphometric variables
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利用景观和形态变量预测大空间尺度水库富营养化状况
DOI：
10.5268/iw-5.3.812
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发表时间：
2015
期刊：
影响因子：
3.1
通讯作者：
Maria Gonzalez
中科院分区：
文献类型：
--
作者：
Lesley Knoll;Elisabeth J. Hagenbuch;Martin Stevens;Michael J. Vanni;William Renwick;J. Denlinger;R. Scott Hale;Maria Gonzalez
摘要水生态系统管理需要了解人为干扰与水生态系统特性之间的联系。由于集水面积与表面积之比（CA：SA）很大，水库往往得到大量的陆地补贴，对富营养化特别敏感。水库数量和随之而来的管理问题正在增加，需要工具来对其富营养化状况进行分类。我们分析了俄亥俄州（美国）的109个水库的数据集，试图利用水库水位特征和水库形态学对富营养化状态进行分类。选择这些预测变量是因为它们相对稳定且易于测量。我们采用回归树分析，并使用一个复合富营养化变量作为我们的响应变量。我们的回归树分析准确地划分了67%的俄亥俄州水库到4个富营养化状态组使用3个预测变量：集水面积的百分比组成的农业与森林;最大水库深度;和CA：SA。我们可以推断，水库集水区包含>71%的森林可能是贫营养到中营养。对于小于71%集水森林的水库，营养状态由集水行作物的相对范围和CA：SA或最大深度确定。我们将回归树应用于环境保护局国家湖泊评估（NLA; n = 339水库）中的水库子集。除了少数例外，我们分类NLA水库的富营养化状态，尽管其广泛的地理范围在美国接壤。研究结果表明，一些易于测量、稳定的参数可以对水库富营养化状态进行分类。像我们这样的模型可能对大规模的管理决策有用。
Abstract Aquatic ecosystem management requires knowledge of the links among landscape-level anthropogenic disturbances and aquatic ecosystem properties. With large catchment area to surface area ratios (CA:SA), reservoirs often receive substantial terrestrial subsidies and can be particularly sensitive to eutrophication. Reservoir numbers and attendant management problems are increasing, and tools are needed to categorize their eutrophication status. We analyzed a dataset of 109 reservoirs in Ohio (USA) in an effort to classify eutrophication status using landscape-level features and reservoir morphometry. These predictor variables were selected because they are relatively stable and easily measured. We employed regression tree analysis and used a composite eutrophication variable as our response variable. Our regression tree analysis accurately divided 67% of Ohio reservoirs into 4 eutrophication status groups using 3 predictor variables: percentage of catchment area composed of agriculture versus forest; maximum reservoir depth; and CA:SA. We can infer that reservoirs with catchments containing >71% forest will likely be oligotrophic to mesotrophic. For reservoirs with <71% catchment forest, trophic status is determined by the relative extent of catchment row crops and either CA:SA or maximum depth. We applied our regression tree to a subset of reservoirs in the Environmental Protection Agency’s National Lakes Assessment (NLA; n = 339 reservoirs). With a few exceptions, we categorized NLA reservoirs by eutrophication status despite their broad geographical range across the contiguous USA. Our results show that a few easily measured, stable parameters can classify reservoir eutrophication status. Models like ours may be useful for broad-scale management decisions.