{"title":"不完善的坡度测量导致湖泊和水库深度几何锥模型的高估","authors":"Jemma Stachelek, Patrick J. Hanly, P. Soranno","doi":"10.1080/20442041.2021.2006553","DOIUrl":null,"url":null,"abstract":"ABSTRACT Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ∼5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. We found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.","PeriodicalId":49061,"journal":{"name":"Inland Waters","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth\",\"authors\":\"Jemma Stachelek, Patrick J. Hanly, P. Soranno\",\"doi\":\"10.1080/20442041.2021.2006553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ∼5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. We found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.\",\"PeriodicalId\":49061,\"journal\":{\"name\":\"Inland Waters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Inland Waters\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1080/20442041.2021.2006553\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"LIMNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inland Waters","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/20442041.2021.2006553","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LIMNOLOGY","Score":null,"Total":0}
Imperfect slope measurements drive overestimation in a geometric cone model of lake and reservoir depth
ABSTRACT Lake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ∼5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. We found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.
期刊介绍:
Inland Waters is the peer-reviewed, scholarly outlet for original papers that advance science within the framework of the International Society of Limnology (SIL). The journal promotes understanding of inland aquatic ecosystems and their management. Subject matter parallels the content of SIL Congresses, and submissions based on presentations are encouraged.
All aspects of physical, chemical, and biological limnology are appropriate, as are papers on applied and regional limnology. The journal also aims to publish articles resulting from plenary lectures presented at SIL Congresses and occasional synthesis articles, as well as issues dedicated to a particular theme, specific water body, or aquatic ecosystem in a geographical area. Publication in the journal is not restricted to SIL members.