Improving representation of sediment fluxes due to glacial retreat in catchment-scale fluvial modelling

Abstract

Fluvial sediment plays a crucial role in shaping riverine ecosystems, supporting aquatic habitats, and maintaining the geomorphological balance of catchment areas. However, both excess sediment and sediment scarcity can have detrimental effects. Excess sedimentation can lead to reduced water quality, reservoir siltation, and increased flood risks, while insufficient sediment supply can result in riverbed erosion, loss of habitat, and destabilization of infrastructure. In glacial regions such as the Mont Blanc Massif, sediment transport dynamics are profoundly influenced by glacier melt processes, which are being altered by climate change. These changes in meltwater contributions introduce significant variability in sediment flux, complicating predictions and management strategies. This study aims to address these challenges by improving the predictive accuracy of the BQART model, a semi-empirical framework widely used to estimate sediment flux. The original BQART model does not account for the influence of glacial meltwater on sediment transport, which can lead to underestimations in glacially dominated catchments. To address this limitation, the model was enhanced by incorporating a melt factor into the glacial erosion parameter (III), reflecting the contribution of snow and ice melt to sediment mobilization. The melt factor was derived using the Positive Degree Day (PDD) method, which calculates meltwater contributions based on regional temperature data. Flow direction and accumulation maps, generated using GIS tools, were employed to ensure accurate delineation of glacier-fed streams within the Mont Blanc Massif catchment basin. The improved BQART model was then applied to recalculate Total Suspended Sediment (TSS) values, and the results were compared against observed data from the Glorise dataset. Statistical analyses, including R-squared (R²) and Root Mean Square Error (RMSE), were used to evaluate model performance and validate the improvements. The results indicate that incorporating the melt factor significantly enhances the model's ability to predict sediment flux during peak melt periods, reducing prediction errors and aligning more closely with observed TSS values. Cumulative Distribution Function (CDF) analysis revealed that the original BQART model tended to overestimate TSS, particularly at higher sediment loads, whereas the improved model provided more accurate predictions across the entire range. This research underscores the critical need to integrate glacial processes, such as meltwater contributions, into sediment transport models to address the impacts of climate change. By improving the accuracy of sediment flux predictions, this study provides valuable insights for managing sediment in glacially influenced river systems, balancing the ecological and infrastructural needs of these vulnerable regions.