Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026240 |
Anomaly kriging helps to remove bias in spatial model runoff estimates | |
Nadir I. Loonat; Albert I. J. M. Van Dijk; Michael F. Hutchinson; Albrecht H. Weerts | |
2020-06-12 | |
发表期刊 | Water Resources Research |
出版年 | 2020 |
英文摘要 | The low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially‐correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e. errors) between prior grid‐based streamflow estimates for Australia at 0.05°×0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally‐hierarchical correction scheme. |
领域 | 资源环境 |
URL | 查看原文 |
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/274381 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Nadir I. Loonat,Albert I. J. M. Van Dijk,Michael F. Hutchinson,et al. Anomaly kriging helps to remove bias in spatial model runoff estimates[J]. Water Resources Research,2020. |
APA | Nadir I. Loonat,Albert I. J. M. Van Dijk,Michael F. Hutchinson,&Albrecht H. Weerts.(2020).Anomaly kriging helps to remove bias in spatial model runoff estimates.Water Resources Research. |
MLA | Nadir I. Loonat,et al."Anomaly kriging helps to remove bias in spatial model runoff estimates".Water Resources Research (2020). |
条目包含的文件 | 条目无相关文件。 |
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