预测气候变化下两个高度冰川化高山集水区的沉积物输出:探索将非参数回归作为一种分析工具

L. Schmidt, T. Francke, Peter Martin Grosse, A. Bronstert
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引用次数: 0

摘要

摘要冰川退化的高山集水区未来悬浮泥沙输出量的变化会影响下游水电站水库、洪水灾害、生态系统和水质。然而,对未来泥沙输出量的定量预测迄今一直受阻于缺乏基于过程的模型,而这种模型能够考虑到决定流域尺度泥沙动态的复杂系统中的所有相关过程。作为一种有前途的替代方法,机器学习(ML)方法最近已成功应用于悬浮泥沙产量(SSYs)建模。据我们所知,本研究是首次探索用机器学习方法来推导 2100 年之前的泥沙输出预测。我们在奥地利厄茨塔尔地区的两个嵌套冰川高山集水区,即 Vent 测量点(98.1 平方公里)和 Vernagt 测量点(11.4 平方公里)上采用了量化回归森林 (QRF)。我们使用温度和降水预测(EURO-CORDEX)以及排水预测(AMUNDSEN 基于物理的水文气候学和积雪模型)作为这两个测站的预测指标。如果预测期间的数值超出了训练数据所代表的范围(观测范围外天数,OOOR),则可能会低估预测结果。为此,我们评估了这些超标的频率和范围,以及由此得出的年平均悬浮泥沙浓度 (SSC) 估算值的敏感性。我们检查了由此得出的 SSY 预测趋势、泥沙峰值的估计时间以及季节分布的变化。结果表明,在 2070 年之前,与 OOOR 数据点相关的不确定性很小(年平均悬浮泥沙浓度估算值的最大变化为 3%)。由于 OOOR 数据点出现的频率更高,而且根据某些预测,到那时冰川将(几乎)消失,这可能会大幅改变该地区的沉积物动态,因此必须更加谨慎地对待 2070 年之后的结果。由此得出的预测结果表明,在未来几十年中,无论排放情景如何,两个测站的沉积物输出都在减少,这意味着沉积物峰值已经过去或正在发生。这与排水量的大幅减少有关,尤其是在夏末冰川融化阶段,这是由于气温升高,冰川萎缩造成的。不过,夏季降水量大也会导致年产量增加,因此在管理沉积物以及洪水灾害等方面需要考虑这两种情况。虽然我们选择的预测因子是沉积物相关过程的替代物,但我们鼓励未来的研究尝试更明确地纳入地貌变化,例如连通性、滑坡、落石或植被定植的变化,因为这些变化可以提高预测的可靠性。
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Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool
Abstract. 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 Ötztal, Austria, above gauge Vent (98.1 km2) and gauge Vernagt (11.4 km2). 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.
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