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| DOI | 10.5194/hess-28-139-2024 |
| Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool | |
| 发表日期 | 2024 |
| ISSN | 1027-5606 |
| EISSN | 1607-7938 |
| 起始页码 | 28 |
| 结束页码 | 1 |
| 卷号 | 28期号:1 |
| 英文摘要 | Future changes in suspended sediment export from deglaciating high-alpine catchments affect downstream hydropower reservoirs, flood hazard, ecosystems and water quality. Yet, quantitative projections of future sediment export have so far been hindered by the lack of process-based models that can take into account all relevant processes within the complex systems determining sediment dynamics at the catchment scale. As a promising alternative, machine-learning (ML) approaches have recently been successfully applied to modeling suspended sediment yields (SSYs).This study is the first, to our knowledge, exploring a machine-learning approach to derive sediment export projections until the year 2100. We employ quantile regression forest (QRF), which proved to be a powerful method to model past SSYs in previous studies, for two nested glaciated high-alpine catchments in the otztal, Austria, above gauge Vent (98.1 km 2 ) and gauge Vernagt (11.4 km 2 ). As predictors, we use temperature and precipitation projections (EURO-CORDEX) and discharge projections (AMUNDSEN physically based hydroclimatological and snow model) for the two gauges. We address uncertainties associated with the known limitation of QRF that underestimates can be expected if values in the projection period exceed the range represented in the training data (out-of-observation-range days, OOOR). For this, we assess the frequency and extent of these exceedances and the sensitivity of the resulting mean annual suspended sediment concentration (SSC) estimates. We examine the resulting SSY projections for trends, the estimated timing of peak sediment and changes in the seasonal distribution.Our results show that the uncertainties associated with the OOOR data points are small before 2070 (max. 3 % change in estimated mean annual SSC). Results after 2070 have to be treated more cautiously as OOOR data points occur more frequently, and glaciers are projected to have (nearly) vanished by then in some projections, which likely substantially alters sediment dynamics in the area. The resulting projections suggest decreasing sediment export at both gauges in the coming decades, regardless of the emission scenario, which implies that peak sediment has already passed or is underway. This is linked to substantial decreases in discharge volumes, especially during the glacier melt phase in late summer, as a result of increasing temperatures and thus shrinking glaciers. Nevertheless, high(er) annual yields can occur in response to heavy summer precipitation, and both developments would need to be considered in managing sediments, as well as e.g., flood hazard. While we chose the predictors to act as proxies for sediment-relevant processes, future studies are encouraged to try and include geomorphological changes more explicitly, e.g., changes in connectivity, landsliding, rockfalls or vegetation colonization, as these could improve the reliability of the projections. |
| 语种 | 英语 |
| WOS研究方向 | Geology ; Water Resources |
| WOS类目 | Geosciences, Multidisciplinary ; Water Resources |
| WOS记录号 | WOS:001168871300001 |
| 来源期刊 | HYDROLOGY AND EARTH SYSTEM SCIENCES
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/306565 |
| 作者单位 | University of Potsdam |
| 推荐引用方式 GB/T 7714 | . Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool[J],2024,28(1). |
| APA | (2024).Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool.HYDROLOGY AND EARTH SYSTEM SCIENCES,28(1). |
| MLA | "Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool".HYDROLOGY AND EARTH SYSTEM SCIENCES 28.1(2024). |
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