气候变化领域集成服务门户(CCPortal): A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections

CCPortal

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 B.D.; Rajagopalan B.; Ray A.J.
发表日期	2020
ISSN	17489318
卷号	15 期号:4
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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	Canonical correlation analysis; Pond count; Prairie pothole region; Predictive model
语种	英语
scopus关键词	Animals; Carbon capture; Climate change; Forecasting; Lakes; Oceanography; Statistical methods; Sustainable development; Canonical correlation analysis; Carbon sequestration; Efficient managements; Fish and wildlife services; Leave-one-out cross validations; Management and conservations; Palmer drought severity indices; Sea surface temperatures; Climate models; carbon sequestration; climate effect; conservation management; drought stress; flooding; pond; prairie; prediction; sea surface temperature; seasonality; severe weather; wetland management; Prairie Pothole Region; United States; Anatidae
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154075
作者单位	Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, CO, United States; Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United States; National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, United States
推荐引用方式 GB/T 7714	Abel B.D.,Rajagopalan B.,Ray A.J.. A predictive model for seasonal pond counts in the United States Prairie Pothole Region using large-scale climate connections[J],2020,15(4).
APA	Abel B.D.,Rajagopalan B.,&Ray A.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 B.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).