SUPERSLUG: Deconstructing sediment superslugs as a legacy of extreme flows

SUPERSLUG将推动科学知识和技术创新的前沿，以揭示对山地景观中灾难性富沉积物流（SRF）遗产的新的基本见解，如山体滑坡，岩石冰雪崩和冰川湖溃决洪水。据推测，由于气候变暖，本世纪灾难性的SRF将变得更加频繁，并且往往对最不发达国家的脆弱社区和资产影响最大。SRF可以携带、移动和存款大量的沉积物，这些沉积物可以覆盖山谷底部数十米的深度。这些沉积物随后的再加工和河流的运输可以产生大规模和快速移动的“超级垃圾”，这是一个所谓的SRF的“遗留”影响。人们对这种遗留影响知之甚少，主要是由于一百多年来持续存在的观测挑战。然而，提高我们对这些影响的理解至关重要：SRF之后沉积物的河流输运增强可能会影响洪水灾害（通过改变河床高程），基础设施（例如通过冲刷桥梁基础和破坏水电涡轮机），并可能破坏水质，降低经历日益不稳定和危险的水文系统的地区的水和能源安全。通过SUPERSLUG，我们试图鼓励围绕我们的论点进行范式转变，即灾难性SRF的景观遗产应该像初始事件一样详细量化。要做到这一点，我们将从我们的国际团队最近由英国研究所资助的试点工作出发，开发和应用一种新的多方法和广泛适用的工具套件，用于量化与决策者相关的几十年时间框架内受SRF影响的集水区的地貌演变，从而产生对沉积物superslugs的基本行为和影响的新见解。我们将集中在印度喜马拉雅山的一个~150公里长的示范系统，最近经历了一场灾难性的SRF;所谓的“Chamoli灾难”。这个流域可以说代表了全球同类地区中数据最丰富的地区，并且位于一个数据极其贫乏的地区。（WP 1）通过使用无人机获得的观测数据，利用卫星遥感从局部观测数据升级到全流域观测数据，对受固体废物影响的系统在空间和时间上的地貌和沉积物演变进行基准测试;（WP 2）使用创新的无线“智能”鹅卵石直接测量受SRF影响的河道中的推移质运动，并辅以被动地震;（WP 3）开发一个开源工具包，利用基于云的（Google Earth Engine）免费处理卫星图像;和（WP 4）整合我们从WP 1 -3的新观测，以提升强大的数值景观演化-水动力学模型来模拟superslug流动性和更广泛的地貌演变，我们的范例集水区。我们的校准模型，这将是一种形式的“数字孪生”，将代表其最大的同类，我们将使用它来探索集水管理决策（例如HEP冲洗时间表），以减轻最严重的超负荷影响。这四个WP的基础是第五个WP，其中我们将采用基于变革理论的方法，与这些新知识和相关工具的受益者密切合作，将我们的研究结果转化为实际成果和影响，包括治理和灾害管理专业人员，水电运营商和更广泛的国际学术界。

SUPERSLUG will push the frontiers of scientific knowledge and technical innovation to reveal new fundamental insights into the legacies of catastrophic sediment-rich flows (SRF) in mountain landscapes, such as landslides, rock-ice avalanches and glacial lake outburst floods. Catastrophic SRFs are hypothesised to become more frequent this century due to climate warming, and often affect vulnerable communities and assets in least developed countries the most. SRFs can entrain, mobilise, and deposit vast quantities of sediment, which can blanket valley floors to depths of tens of metres. The subsequent re-working and transport of these sediments by rivers can generate large-scale and fast-moving 'superslugs', which is a so-called 'legacy' impact of an SRF. Such legacy impacts are poorly understood, mostly due to observational challenges which have persisted for over a hundred years. However, improving our understanding of these impacts is of vital importance: enhanced fluvial transport of sediment following an SRF can affect flood hazard (by altering river channel bed elevation), infrastructure (e.g. by scouring bridge footings and damaging hydropower turbines), and can disrupt water quality, reducing water and energy security in regions that experience increasingly unstable and hazardous hydrological regimes. With SUPERSLUG we seek to encourage a paradigm shift framed around our argument that the landscape legacies of catastrophic SRFs should be quantified in as much detail as an initial event. To do this we will springboard from recent UKRI-funded pilot work by our international team to develop and apply a new multi-method and widely applicable suite of tools for quantifying the geomorphological evolution of SRF-affected catchments over multi-decade timeframes that are relevant for decision makers, in turn generating new insights into the fundamental behaviour, and impacts, of sediment superslugs. We will focus on a ~150 km-long exemplar system in the Indian Himalaya that has recently experienced a catastrophic SRF; the so-called 'Chamoli disaster'. This catchment arguably represents the most data-rich landscape of its type globally and sits within an otherwise extremely data-poor region. To deconstruct the evolution and impacts of sediment superslugs we will implement five work packages which will: (WP1) benchmark the geomorphological and sedimentological evolution of an SRF-affected system in space and time by using drone-derived observations to upscale from local- to catchment-wide observations using satellite remote sensing; (WP2) directly measure bedload motion in SRF-affected river channels using innovative wireless 'smart' cobbles, complemented with passive seismics; (WP3) develop an open-source toolkit for detecting and tracking fine-grained superslugs by leveraging cloud-based (Google Earth Engine) processing of free satellite imagery; and (WP4) integrate our novel observations from WP1-3 to upscale a powerful numerical landscape evolution-hydrodynamic model to simulate superslug mobility and the wider geomorphological evolution of our exemplar catchment. Our calibrated model, which will be a form of 'digital twin', will represent the largest of its kind and we will use it to explore catchment management decisions (e.g. HEP flushing schedules) for mitigating the worst superslug impacts. Underpinning these four WPs is a fifth WP, wherein we will adopt a Theory of Change-based approach for engaging closely with beneficiaries of this new knowledge and associated tools to translate our findings into practical outcomes and impact, including governance and disaster management professionals, hydropower operators and the wider international academic community.