Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2017.11.001 |
Predicting stream vulnerability to urbanization stress with Bayesian network models | |
Weil, Kristen K.1,6; Cronan, Christopher S.1; Meyer, Spencer R.2; Lilieholm, Robert J.3; Danielson, Thomas J.4; Tsomides, Leonidas4; Owen, Dave5 | |
2018-02-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING
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ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2018 |
卷号 | 170页码:138-149 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | As human development and urbanization expand across the landscape, increasing numbers of streams are threatened with impairment from disturbance and stresses associated with land use changes. In this investigation, a Bayesian Network (BN) with an expert-informed model structure was developed to predict stream vulnerability to urbanization across a range of biophysical conditions. Primary factors affecting vulnerability were stream buffers, colonization connectivity, agriculture, watershed area, and sand/gravel aquifers. On a scale from 0 to 100 (lowest to highest probability), BN model vulnerability scores ranged from a minimum of 20 to a maximum of 87.5 across the 23,554 stream catchments in our statewide study area. Catchment vulnerability scores were linked with predictions of land development suitability from a second BN model in order to map the locations of streams at risk of impairment from projected future urbanization in two large watersheds in Maine, USA. Our BN synthesis identified 5% of the streams that are at risk based on two assessment criteria: (1) their catchments have projected future impervious cover (IC) levels greater than 6% and (2) the stream catchments have predicted vulnerability scores in the highest quartile of the BN model probability distribution. These at-risk streams represent priority targets for proactive monitoring, management, and conservation efforts to avoid future degradation and expensive restoration costs. This study laid the conceptual groundwork for using BN spatial models to identify streams that are not only vulnerable to urbanization, but are also located in catchments classified with a high probability of development suitability and future urbanization. |
英文关键词 | Stream vulnerability Watershed urbanization Resilience Bayesian networks Spatial models Sustainable stream protection |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000419412400013 |
WOS关键词 | MULTIPLE SPATIAL SCALES ; SUSTAINABILITY SCIENCE ; BELIEF NETWORKS ; BIOLOGICAL CONDITION ; INSECT COMMUNITIES ; EXPERT KNOWLEDGE ; LAND-USE ; FISH ; CONSERVATION ; MANAGEMENT |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
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/25201 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Maine, Sch Biol & Ecol, Deering Hall, Orono, ME 04469 USA; 2.Highstead Fdn, POB 1097, Redding, CT 06875 USA; 3.Univ Maine, Sch Forest Resources, Orono, ME 04469 USA; 4.Maine Dept Environm Protect, 17 State House Stn, Augusta, ME 04333 USA; 5.Univ Calif San Francisco, Hastings Coll Law, San Francisco, CA 94706 USA; 6.Trust Publ Land, 607 Cerillos Rd, Santa Fe, NM 87505 USA |
推荐引用方式 GB/T 7714 | Weil, Kristen K.,Cronan, Christopher S.,Meyer, Spencer R.,et al. Predicting stream vulnerability to urbanization stress with Bayesian network models[J]. LANDSCAPE AND URBAN PLANNING,2018,170:138-149. |
APA | Weil, Kristen K..,Cronan, Christopher S..,Meyer, Spencer R..,Lilieholm, Robert J..,Danielson, Thomas J..,...&Owen, Dave.(2018).Predicting stream vulnerability to urbanization stress with Bayesian network models.LANDSCAPE AND URBAN PLANNING,170,138-149. |
MLA | Weil, Kristen K.,et al."Predicting stream vulnerability to urbanization stress with Bayesian network models".LANDSCAPE AND URBAN PLANNING 170(2018):138-149. |
条目包含的文件 | 条目无相关文件。 |
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