Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab7465 |
A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections | |
Abel, Benjamin D.1; Rajagopalan, Balaji1,2; Ray, Andrea J.3 | |
2020-04-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS
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ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | The Prairie Pothole Region (PPR), located in central North America, is an important region hydrologically and ecologically. Millions of wetlands, many containing ponds, are located here, and they serve as habitats for various biota and breeding grounds for waterfowl. They also provide carbon sequestration, sediment and nutrient attenuation, and floodwater storage. Land modification and climate change are threatening the PPR, and water and wildlife managers face important conservation decisions due to these threats. We developed predictive, multisite forecasting models using canonical correlation analysis (CCA) for pond counts in the southeast PPR, the portion located within the United States, to aid in these important decisions. These forecast models predict spring (May) and summer (July) pond counts for each region (stratum) of the United States Fish and Wildlife Service's pond and waterfowl surveys using a suite of antecedent, large-scale climate variables and indices including 500 millibar heights, sea surface temperatures (SSTs), and Palmer Drought Severity Index (PDSI). Models were developed to issue forecasts at the start of all preceding months beginning on March 1st. The models were evaluated for their performance in a predictive mode by leave-one-out cross-validation. The models exhibited good performance (R values above 0.6 for May forecasts and 0.4 for July forecasts), with performance increasing as lead time decreased. This simple and versatile modeling approach offers a robust tool for efficient management and sustainability of ecology and natural resources. It demonstrates the ability to use large-scale climate variables to predict a local variable in a skilful way and could serve as an example to develop similar models for use in management and conservation decisions in other regions and sectors of the environment. |
英文关键词 | Prairie Pothole Region canonical correlation analysis predictive model pond count |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000523508000001 |
WOS关键词 | SURFACE-TEMPERATURE ; NORTH-DAKOTA ; WATER LEVELS ; WETLANDS ; HYDROLOGY ; SST |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/279300 |
专题 | 气候变化 |
作者单位 | 1.Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO 80309 USA; 2.Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA; 3.NOAA, Earth Syst Res Lab, Div Phys Sci, Boulder, CO USA |
推荐引用方式 GB/T 7714 | Abel, Benjamin D.,Rajagopalan, Balaji,Ray, Andrea J.. A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(4). |
APA | Abel, Benjamin D.,Rajagopalan, Balaji,&Ray, Andrea J..(2020).A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections.ENVIRONMENTAL RESEARCH LETTERS,15(4). |
MLA | Abel, Benjamin D.,et al."A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections".ENVIRONMENTAL RESEARCH LETTERS 15.4(2020). |
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