Coupling glacial lake impact, dam breach, and flood processes: A modeling perspective

Glacial lake outburst floods (GLOFs) are highly mobile mixtures of water and sediment that occur suddenly and are capable of traveling tens to hundreds of kilometers with peak discharges and volumes several orders of magnitude larger than those of normal floods. They travel along existing river channels, in some instances into populated downstream regions, and thus pose a risk to people and infrastructure. Many recent events involve process chains, such as mass movements impacting glacial lakes and triggering dam breaches with subsequent outburst floods. A concern is that effects of climate change and associated increased instability of high mountain slopes may exacerbate such process chains and associated extreme flows. Modeling tools can be used to assess the hazard of potential future GLOFs, and process modeling can provide insights into complex processes that are difficult to observe in nature. A number of numerical models have been developed and applied to simulate different types of extreme flows, but such modeling faces challenges stemming from a lack of process understanding and difficulties in measuring extreme flows for calibration purposes. Here we review the state of knowledge of key aspects of modeling GLOFs, with a focus on process cascades. Analysis and simulation of the onset, propagation, and potential impact of GLOFs are based on illustrative case studies. Numerical models are presently available for simulating impact waves in lakes, dam failures, and flow propagation but have been used only to a limited extent for integrated simulations of process cascades. We present a spectrum of case studies from Patagonia, the European Alps, central Asia, and the Himalayas in which we simulate single processes and process chains of past and potential future events. We conclude that process understanding and process chain modeling need to be strengthened and that research efforts should focus on a more integrative treatment of processes in numerical models.