{"title":"气温数据源对山区溪流温度统计模型推断的影响","authors":"Daniel J. Isaak, Dona L. Horan, Sherry P. Wollrab","doi":"10.1016/j.hydroa.2024.100172","DOIUrl":null,"url":null,"abstract":"<div><p>Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (T<sub>w</sub>). Air temperature data are broadly available and sourced from sensors paired with T<sub>w</sub> sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily T<sub>w</sub> records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among T<sub>w</sub> studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.</p></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589915524000026/pdfft?md5=11fd939c8e5f90acdaf3ccb06e410169&pid=1-s2.0-S2589915524000026-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Air temperature data source affects inference from statistical stream temperature models in mountainous terrain\",\"authors\":\"Daniel J. Isaak, Dona L. Horan, Sherry P. Wollrab\",\"doi\":\"10.1016/j.hydroa.2024.100172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (T<sub>w</sub>). Air temperature data are broadly available and sourced from sensors paired with T<sub>w</sub> sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily T<sub>w</sub> records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among T<sub>w</sub> studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.</p></div>\",\"PeriodicalId\":36948,\"journal\":{\"name\":\"Journal of Hydrology X\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2589915524000026/pdfft?md5=11fd939c8e5f90acdaf3ccb06e410169&pid=1-s2.0-S2589915524000026-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology X\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589915524000026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology X","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589915524000026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
溪流温度控制着许多生物物理过程,经常成为建模工作的主题,以了解和预测对流域条件、生境改变和气候变化的反应。气温(AT)经常被用于温度统计模型,作为物理过程的协变量替代物,因为它与水温(Tw)的时空变化密切相关。气温数据来源广泛,包括与 Tw 站点配对的传感器、远程气象站和网格气候数据集,但人们对这些数据来源的取舍以及复杂地形山区环境中的微气候变化如何影响模型推断的认识往往有限。为了解决这些问题,我们在山区河流网络的 13 个站点收集了每日 Tw 记录,将这些记录与北美大部分地区 11 个来源的 AT 数据联系起来,并拟合线性回归模型,以评估预测性能和参数估计的一致性。尽管这些模型的预测准确性普遍较高,但对 AT 斜坡参数(通常被解释为热敏感性)的估计却因 AT 数据源的不同而有很大差异。这些结果影响了沼泽研究中估算值的可比性,并凸显了山区地貌溪流温度建模所面临的挑战。虽然没有一种自动取水数据源是理想的,但对于特定的使用情况,有些数据源比其他数据源更有优势,我们就此问题提出了一般性建议。
Air temperature data source affects inference from statistical stream temperature models in mountainous terrain
Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (Tw). Air temperature data are broadly available and sourced from sensors paired with Tw sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily Tw records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among Tw studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.