H126
Statistical Methods for Modeling Changes in Flood Characteristics: Novel Approaches and Comparisons

Session ID#: 81731

Session Description:
While a plethora of methods have been devised to model observed and anticipated changes in flood characteristics (e.g. frequency, magnitude, duration and volume), many open questions remain regarding the choice of methods under different circumstances. For instance, when is it worth taking trends in the variability of flood characteristics into account? What are the implications of choosing different indicators of land cover change when assessing anthropogenic impacts on flooding? How can we responsibly utilize simulations of future environmental conditions when projecting changes in floods? This section encourages both comparisons of established methods and the introduction of novel ones, including methods that combine statistical approaches with mechanistic hydrologic models or advanced computational tools, such as machine learning.
Co-Organized:
  • GC - Global Environmental Change
  • NH - Natural Hazards
Index Terms:

1803 Anthropogenic effects [HYDROLOGY]
1817 Extreme events [HYDROLOGY]
1821 Floods [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Primary Convener:  Jory S Hecht, University of Vermont, Vermont EPSCoR, Burlington, VT, United States
Conveners:  Stacey A Archfield, US Geological Survey, Reston, VA, United States and Karen R Ryberg, USGS Dakota Water Science Center, Bismarck, ND, United States
Primary Liaison:  Jory S Hecht, University of Vermont, Vermont EPSCoR, Burlington, VT, United States
 
The Spatial Dependence of Flood Hazard and Risk in the United States and its potential for change (Invited) (491382)
Paul D Bates1, Niall Quinn2, Christopher Charles Sampson3, Oliver Wing4, Andy Smith2 and Jeffrey C Neal5, (1)University of Bristol, School of Geographical Sciences, Bristol, BS8, United Kingdom, (2)Fathom, Bristol, United Kingdom, (3)University of Bristol, Bristol, United Kingdom, (4)SSBN Ltd, Bristol, United Kingdom, (5)University of Bristol, School of Geographical Sciences, Bristol, United Kingdom
 
Estimation of the Flood Discharge Considering the Hydrological Characteristics of Urban Stream in Island area (491985)
Sung-Kee Yang1, Bo Seong Kang2 and Myung Soo Kang2, (1)Jeju National University, Civil Engineering, Jeju, Korea, Republic of (South), (2)Jeju National University, Civil Engineering, Jeju, South Korea
 
Comparing Long-Term Flood Projection Approaches in United States River Basins (Invited) (494910)
Katherine Schlef, Western New England University, Civil and Environmental Engineering, Springfield, MA, United States, Baptiste Francois, University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States and Casey Brown, University of Massachusetts Amherst, Civil and Environmental Engineering, Amherst, MA, United States
 
A Spatially Consistent Bias Correction Technique for Distributed Streamflow Modeling (495661)
Bart Nijssen, University of Washington, Seattle, WA, United States, Andrew Bennett, University of Washington Seattle Campus, Department of Civil and Environmental Engineering, Seattle, WA, United States and Marketa McGuire, Bureau of Reclamation Denver, Denver, CO, United States
 
Nonstationary Flood Frequency Analysis: A Mixed and Pooled Approach (506487)
Philip Yates, DePaul University, Chicago, IL, United States
 
Using Machine Learning and Remotely Sensed Data to Develop Spatiotemporal Maps of Flood Surge and Retreat for the Coastal Plain of North Carolina (510138)
Emine Fidan and Natalie Nelson, North Carolina State University, Biological and Agricultural Engineering, Raleigh, NC, United States
 
Stationary Hydrologic Frequency Analysis for Non-stationary Datasets by Incorporating Uncertainty Assessment (511868)
Cuauhtemoc Tonatiuh Vidrio Sahagun and Jianxun He, University of Calgary, Department of Civil Engineering, Calgary, AB, Canada
 
Bare-earth DEM Generation in Urban Areas Based on a Machine Learning Method (515191)
Yinxue Liu, University of Bristol, Bristol, BS8, United Kingdom, Paul D Bates, University of Bristol, School of Geographical Sciences, Bristol, BS8, United Kingdom, Jeffrey C Neal, University of Bristol, School of Geographical Sciences, Bristol, United Kingdom and Dai Yamazaki, The University of Tokyo, Institute of Industrial Sciences, Tokyo, Japan
 
Flood nonstationarity across the US: Detection, attribution, and adjustment (516472)
Karen R Ryberg1,2, Jory S Hecht3, Nancy A Barth2, Stacey A Archfield4, Annalise G Blum5, Katherine J Chase6, Robert W Dudley7, Angela E Gregory8, Glenn A Hodgkins7, Steven K Sando9 and Thomas R Sando6, (1)USGS North Dakota Water Science Center, Bismarck, ND, United States, (2)USGS Dakota Water Science Center, Bismarck, ND, United States, (3)USGS Analysis and Prediction Branch, Reston, VT, United States, (4)US Geological Survey, Reston, VA, United States, (5)Tufts University, Medford, MA, United States, (6)USGS WY-MT Water Science Center, Helena, United States, (7)US Geological Survey, Augusta, ME, United States, (8)USGS Dakota Water Science Center, Bismarck, United States, (9)USGS WY-MT Water Science Center, Helena, MT, United States
 
Detecting the homogeneous regions for real-time flood inundation mapping using rating curve similarity (519778)
Keighobad Jafarzadegan, University of Alabama, Department of Civil, Construction, and Environmental Engineering, Tuscaloosa, AL, United States and Hamid Moradkhani, The University of Alabama, Tuscaloosa, AL, United States
 
The Effect of Storm Direction and Motion on Flood Frequency Analysis Using Physically-Based Streamflow Simulations (542963)
Gabriel Perez1, Ricardo Mantilla1, Witold F Krajewski1 and Daniel B. Wright2, (1)University of Iowa, Iowa City, IA, United States, (2)University of Wisconsin Madison, Madison, WI, United States
 
Rapid Increase in the Compound Droughts and Heat Waves in China (565703)
Lei Ye1, Ke Shi1, Zhuohang Xin1, Chao Wang2 and Chi Zhang3, (1)Dalian University of Technology, Dalian, China, (2)China Institute of Water Resources and Hydropower Research, Beijing, China, (3)Dalian University of Technology, School of Hydraulic Engineering, Dalian, China
 
A Probabilistic Framework for Modeling Flood Risk using Monte Carlo Simulation and a Novel Convolution Reduction Algorithm (587140)
Shane M Putnam1, Seth Lawler1, Siamak Esfandiary2 and Mathew Mampara1, (1)Dewberry, Fairfax, VA, United States, (2)Federal Emergency Management Agency, Washington, DC, United States
 
Revising Flood-Frequency Curves under Climate Change in the United States (587821)
Chandramauli Awasthi, North Carolina State University Raleigh, Raleigh, NC, United States, Stacey A Archfield, US Geological Survey, Reston, VA, United States, Julie E. Kiang, US Geological Survey, Office of Surface Water, Reston, VA, United States, Karen R Ryberg, USGS Dakota Water Science Center, Bismarck, ND, United States and Sankarasubramanian Arumugam, NC State University, Raleigh, NC, United States
 
Impact of Catchment Grouping using Machine Learning on Estimating Envelope Curves of Extreme Floods in Alpine Catchments (607557)
Ricardo Tavares da Costa1, Elena Toth1, Simone Persiano Sr2, Alberto Viglione3, Miriam Bertola4, Guenter Bloeschl5 and Attilio Castellarin6, (1)University of Bologna, Bologna, Italy, (2)University of Bologna, DICAM, Bologna, Italy, (3)Institute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Vienna, Austria, (4)Vienna University of Technology, Vienna, Austria, (5)Vienna University of Technology, Institute of Hydraulic Engineering and Water Resources Management, Vienna, Austria, (6)Università di Bologna, DICAM, Bologna, Italy
 
On the Variability of Flood Attributes and Damages at the Global Scale (607875)
Nasser Najibi, CUNY City College of New York, New York, NY, United States and Naresh Devineni, CUNY City College, Department of Civil Engineering, New York, NY, United States
 
How many flood events occur in the United States every year? Challenges in Defining Flood Events and Estimating their Frequency. (629123)
Keith A Sawicz, Kiran Chinnayakanahalli and Hemant Chowdhary, AIR Worldwide Boston, Research, Boston, MA, United States
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