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Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management 
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下载免费PDF全文 There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km2 river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between‐river and between‐reach variability in all temperature metrics in lower‐order rivers indicating that increased monitoring effort should be focussed at these smaller scales. The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature. The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies. 相似文献
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薄盘光滑样条插值中三种协变量方法的降水量插值精度比较 总被引：4，自引：0，他引：4
在薄盘光滑样条插值中,高相关协变量的选取决定了插值结果的精确性。以2001-2009年全国728个气象站点日降水为数据源,提取年降水量数据,在分析多年平均降水量与两协变量高程（DEM）和距海岸线距离（DCL）的空间相关性基础上,利用ANUSPLIN软件,比较不同协变量下降水量插值结果精度在全国尺度以及区域尺度上的差异。以DEM、DCL及DEM-DCL分别为协变量对降水量数据进行空间插值发现：①在全国尺度上,DEM法的平均绝对误差（MAE）为47.79,略低于DEM-DCL法（48.90）,但显著低于DCL法（55.54）;且DEM法的平均相对误差和均方根误差也明显低于其它两种方法。②在区域尺度上,除西藏地区外的其他7个区域,3种方法的插值误差与全国尺度上相一致。西藏地区降水插值结果以DCL法的精度最高,而DEM法则较差。研究建议除在西藏地区的降水量插值研究中采用DCL法,在全国其他大部分区域采用DEM法。 相似文献
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A framework to estimate sediment loads based on the statistical distribution of sediment concentrations and various functional forms relating distribution characteristics (e.g. mean and variance) to covariates is developed. The covariates are used as surrogates to represent the main processes involved in sediment generation and transport. Statistical models of increasing complexity are built and compared to assess their relative performance using available sediment concentration and covariate data. Application to the Beaurivage River watershed (Québec, Canada) is conducted using data for the 1989–2004 period. The covariates considered in this application are streamflow and calendar day. A comparison of different statistical models shows that, in this case, the log‐normal distribution with a mean value depending on streamflow (power law with an additive term) and calendar day (sinusoidal), a constant coefficient of variation for streamflow dependence and a constant standard deviation for calendar day dependence provide the best result. Model parameters are estimated using the maximum likelihood estimation technique. The selected model is then used to estimate the distribution of annual sediment loads for the Beaurivage River watershed for a selected period. A bootstrap parametric method is implemented to account for uncertainties in parameter values and to build the distributions of annual loads. Comparison of model results with estimates obtained using the empirical ratio estimator shows that the latter were rarely within the 0·1–0·9 quantile interval of the distributions obtained with the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
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数字土壤制图研究综述与展望 总被引：4，自引：0，他引：4
土壤的空间分布是土壤形成与发展过程的体现。数字土壤制图是一种新兴的、高效表达土壤空间分布的技术方法,在过去的30年取得了飞速发展。其理论基础为土壤成土因子学说和地理学第一定律。国内外学者在获取环境变量数据、采样方法、制图模型方法和土壤图产生及评价方面开展了大量的研究,应用案例也从小范围到大区域,甚至是全球尺度。未来数字土壤制图的发展方向包括：环境变量刻画的新技术,特别是体现人类活动方面的环境因子;新型数据和遗留数据的有效利用;土壤发生学知识与数学模型的紧密结合的新型推理方法;支持大数据多终端的计算模式。 相似文献
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