Topographic response to the transition from snowmelt- to rainfall- triggered extremes

2013年9月，科罗拉多州博尔德附近的山麓发生了一场“千年一遇”的风暴，引发了大范围的山体滑坡和洪水。然而，从极端降雨到洪水再到河流侵蚀和山体滑坡的因果链是复杂的。例如，这一事件产生了“25至200年一遇”的洪水和山体滑坡，在当地消除了100至1000年的沉积物。这个项目的重点是如何在山区景观的两个主要的径流产生模式（融雪和降雨径流）可以导致侵蚀过程的速度和模式的诊断差异。我们使用的科罗拉多前山脉，美国作为一个案例研究，平均径流系统地增加，由于增加融雪，而峰值径流是最高的中间海拔造成的大，强降雨事件。更好地解释海拔依赖的径流产生对水资源管理、洪水和侵蚀危害评估以及景观演变建模具有重要意义。为此，该项目促进了博尔德市科罗拉多大学三个研究所之间的合作（博尔德溪临界区观测站、地球实验室和社区地表动力学建模系统），通过向研究人员、决策者、该项目的主要目标是为河流切割制定一个定量框架，该框架说明了科罗拉多前山脉的两个有据可查的属性：（1）从融雪到降雨的地形转变引发了极端事件，（2）高海拔、低地形、侵蚀面的形成和持续，基岩河流峡谷切入其中。 虽然科罗拉多前沿山脉是重点，但该方法可转移到事件尺度径流变化中具有强地形梯度的其他设置（例如，安第斯山脉;喜马拉雅山脉）。为了实现目标，研究任务包括三个主要组成部分：（A）水文气候数据分析;（B）地形数据分析;（C）河流下切的数值模拟。水文气候数据分析的重点是如何亚日常到日常（即，强度，持续时间）和空间（即，风暴大小）降雨和融雪事件的属性会影响洪水频率。地形数据分析的重点是根据实地调查和摄影测量的数据，沿着博尔德溪和壁垒山脉的高分辨率机载激光雷达地形，以及中等分辨率的区域地形，量化通道陡度，通道宽度和粒度分布在景观中的变化。河流下切的数值模拟建立在基岩下切的一维随机阈值模型上，该模型目前仅包括随机径流强度，通过添加更多的随机参数（即，径流产生的位置和空间足迹）。降雨和融雪统计中的地形梯度的明确说明及其在设定河流对基准面下降的响应时间尺度中的作用，为气候、地形和侵蚀之间先前未被认识到的反馈提供了第一次测试。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

In September of 2013, a "1000-yr" storm occurred in the foothills of the mountains near Boulder, CO, triggering widespread landslides and floods. However, the causal chain from extreme rainfall to flooding to river erosion and landslides is complex. For example, this event produced "25- to 200- yr" floods and landslides locally removed 100's to 1000's of years of sediment production. This project focuses on how the two main modes of runoff generation (snowmelt and rainfall runoff) in mountain landscapes can lead to diagnostic differences in the rates and patterns of erosional processes. We use the Colorado Front Range, USA as a case study where mean runoff systematically increases with elevation due to increased snowmelt, while peak runoff is highest at intermediate elevations caused by large, intense rainfall events. Better accounting for elevation-dependent runoff generation has implications for water resource management, flood and erosion hazard assessment, and landscape evolution modeling. To this end, this project fosters collaboration among three institutes at the University of Colorado, Boulder (the Boulder Creek Critical Zone Observatory, Earth Lab, and the Community Surface Dynamics Modeling System) to better integrate data collection, analysis, and model development via open, reproducible workflows made available to researchers, decision-makers, and citizens.The main goals of this project are to develop a quantitative framework for river incision that accounts for two well-documented attributes of the Colorado Front Range: (1) an orographic transition from snowmelt- to rainfall- triggered extreme events, and (2) the formation and persistence of high elevation, low relief, erosion surfaces, into which bedrock river canyons have incised. While the Colorado Front Range is the focus, the approach is transferable to other settings with strong orographic gradients in event-scale runoff variability (e.g., Andes; Himalayas). To accomplish objectives, research tasks consist of three main components: (A) Hydroclimatic data analysis; (B) Topographic data analysis; (C) Numerical modeling of river incision. Hydroclimatic data analysis focuses on how sub-daily to daily (i.e., intensity, duration) and spatial (i.e., storm size) attributes of rainfall and snowmelt events imprint into flood frequencies. Topographic data analysis focuses on quantifying how channel steepness, channel width, and grain size distributions vary in the landscape based on data from field surveys and photogrammetry, high-resolution airborne lidar topography along Boulder Creek and the Rampart Range, and moderate-resolution regional topography. Numerical modeling of river incision builds on a 1-D stochastic-threshold model of bedrock incision, that currently only includes stochastic runoff intensity, by adding more stochastic parameters (i.e., the location and spatial footprint of runoff generation). Explicit accounting for orographic gradients in rainfall and snowmelt statistics and their role in setting the timescale of river response to base level fall provides a first test of a previously unrecognized feedback between climate, topography, and erosion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.