TESTING MODELS FOR RIVER AVULSION STYLE WITH REMOTE SENSING DATA AND NUMERICAL SIMULATIONS

最引人注目但却很少有人目睹的事件之一是河流在洪泛区的一个新地点冲刷（或移动）。河流的冲刷可能是危险的，因为它们会移动大量的沉积物，造成大面积的洪水。尽管如此，目前尚不清楚为什么有些撕脱是危险的，而有些则不是。这项研究将开发新的遥感方法来检测撕脱，并使用数值模拟来验证为什么有些撕脱很大，会造成显著的景观变化和洪水，而另一些则很小。该团队将以跨学科的方法培训本科生和研究生，并利用研究成果为小学高年级至初中的儿童开发新的科学课程，以提高他们在高级水平上学习地球科学的可能性。提出的研究旨在通过使用遥感记录和数值模拟来测试河流冲刷如何在洪泛区移动的模型，从而推进河流冲刷科学。该团队将测试崩裂的风格是否会在下游发生变化；初步数据表明，靠近山前的撕裂倾向于重新占据旧的河道，而距离较远的则倾向于创造自己的河道。这是因为泥沙在下游变小，更细的泥沙更容易被运到河岸上的洪泛区，填满废弃的河道，迫使冲刷形成新的河道。该假设将通过以下几个方面得到验证：1)利用谷歌Earth Engine等大数据遥感工具，通过Landsat数据搜索，探测现代前陆盆地的撕裂作用；2)利用我们的指纹识别算法，量化撕裂作用重新形成或形成自己的通道的趋势，从而隔离受撕裂作用干扰的区域；3)建立包括下游细化和岸上沉积在内的细胞撕裂作用模型。该模型将用于测试下游细化与撕裂行为之间的联系。该奖项由地貌学和土地利用动力学（GLD）和极端事件预测和恢复能力（PREEVENTS）项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

One of the most dramatic, yet rarely witnessed, events is when a river avulses (or moves) to a new spot in a floodplain. River avulsions can be hazardous because they move incredible volumes of sediment and cause extensive flooding. Despite this, it is not known why some avulsions are hazardous and others are not. This research will develop new remote sensing methods to detect avulsions and test theories using numerical modeling for why some avulsions are large and create significant landscape change and floods, and others are small. The team will engage in training undergraduate and graduate students in cross-disciplinary methods, and also use the research results to develop new science curriculum for children in upper elementary to middle school grades that is aimed at increasing likelihood of pursuing Earth Science at advanced levels.The proposed research aims to advance the science of river avulsions by using the remote sensing record and numerical simulations to test models for how avulsions move across the floodplain. The team will test if avulsion style changes moving downstream; preliminary data suggests that near the mountain front avulsions tend to reoccupy older channels, while those farther away tend to create their own channels. This arises because sediment size decreases downstream, and finer sediment is more easily transported overbank into the floodplain, filling abandoned channels, and forcing avulsions to create new channels. The hypothesis will be tested by 1) detecting avulsions in modern foreland basins by using big data remote sensing tools, e.g., Google Earth Engine, to search through Landsat data, 2) quantifying avulsion tendency to reoccupy or create their own channel with our fingerprinting algorithm that isolates the area disturbed by the avulsion, and 3) building a cellular avulsion model that includes downstream fining and overbank sedimentation. The model will be used to test the connection between downstream fining and avulsion behavior. This award is cofunded by the Geomorphology and Land-use Dynamics (GLD) and Prediction of and Resilience against Extreme Events (PREEVENTS) programs.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.