Preprints
https://doi.org/10.5194/egusphere-2023-1063
https://doi.org/10.5194/egusphere-2023-1063
29 Jun 2023
 | 29 Jun 2023

Projecting sediment export from two highly glacierized alpine catchments under climate change: Exploring non-parametric regression as an analysis tool

Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert

Abstract. Future changes in suspended sediment export from deglaciating high-alpine catchments affect downstream hydropower and reservoirs, flood hazard, ecosystems and water quality. Yet so far, quantitative projections of future sediment export have been hindered by the lack of physical 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 (SSY).

This study is the first to our knowledge exploring 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 SSY in previous studies, at two nested high-alpine gauges in the Ötztal, Austria, i.e. gauge Vent (98.1 km² catchment area, 28 % glacier cover in 2015) and gauge Vernagt (11.4 km² catchment area, 64 % glacier cover). 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 a known limitation of QRF, i.e. that underestimates can be expected if out-of-observation-range (OOOR) data points (i.e. values exceeding the range represented in the training data) occur in the application period. 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 as 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. 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.

Lena Katharina Schmidt et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1063', Mustafa Al-Mukhtar, 21 Jul 2023
    • AC1: 'Reply on RC1', Lena Katharina Schmidt, 30 Aug 2023
  • RC2: 'Comment on egusphere-2023-1063', Anonymous Referee #2, 27 Jul 2023
    • AC2: 'Reply on RC2', Lena Katharina Schmidt, 30 Aug 2023
  • RC3: 'Comment on egusphere-2023-1063', Anonymous Referee #3, 28 Jul 2023
    • AC3: 'Reply on RC3', Lena Katharina Schmidt, 30 Aug 2023

Lena Katharina Schmidt et al.

Lena Katharina Schmidt et al.

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Short summary
How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable, as many interacting processes are involved and appropriate physical models are lacking. We present the first study to our knowledge exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that ‘peak sediment’ may have already passed.