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

最戏剧性的，但很少看到的事件之一是当一条河流撕脱（或移动）到一个新的地点在洪泛平原。河流撕裂可能是危险的，因为它们移动了令人难以置信的沉积物体积，并导致大面积洪水。尽管如此，为什么有些撕脱是危险的，而另一些则不是，目前还不清楚。这项研究将开发新的遥感方法来检测撕脱和测试理论，使用数值模拟为什么有些撕脱是大的，并创造显着的景观变化和洪水，和其他人是小的。该小组将从事跨学科方法的本科生和研究生培训，并利用研究成果为小学高年级到中学的孩子们开发新的科学课程，旨在增加在高级水平上追求地球科学的可能性。拟议的研究旨在通过使用遥感记录和数值模拟来测试模型，以提高河流撕脱的科学。撕脱物在漫滩上移动研究小组将测试下游的撕脱类型是否会发生变化;初步数据表明，靠近山前的撕脱倾向于重新占据旧的通道，而那些更远的则倾向于创建自己的通道。这是因为沉积物的尺寸在下游减小，较细的沉积物更容易越过河岸进入洪泛区，填充废弃的河道，并迫使撕裂形成新的河道。该假设将通过以下方式进行验证：1）使用大数据遥感工具检测现代前陆盆地中的撕脱，例如，谷歌地球引擎，通过陆地卫星数据搜索，2）量化撕脱倾向，重新占据或创建自己的通道与我们的指纹算法，隔离的撕脱干扰的区域，和3）建立一个细胞撕脱模型，包括下游罚款和漫滩沉积。该模型将用于测试下游变细和撕脱行为之间的联系。该奖项由地貌学和土地利用动力学（GLD）以及极端事件预测和恢复力（PREEVENTS）项目共同资助。该奖项反映了NSF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.