H021
Advancing Hydrological Modeling and Prediction Using Large-domain Meteorological and Hydrological Datasets and Models

Session ID#: 160716

Session Description:
As observing infrastructure and computational capabilities improve, numerous large-domain (e.g., nation, continental, global) datasets are continuously developed based on data from stations, remote sensing, and reanalysis/climate models. The new information that is available covers various spatiotemporal scales and domains throughout the soil-vegetation-atmosphere continuum. The development of new data products plays a vital role in supporting process-based and data-driven hydrological modeling and in improving operational prediction systems. However, existing datasets still face problems of inadequacy and/or inaccuracy, especially in remote areas. The uncertainty quantification of large-domain datasets is still in its infancy, and is particularly needed under a changing climate. There are also increasing efforts exploring applications of these new large-domain datasets in hydrological modeling and in predictions of hydrological extremes (e.g., flood and drought). 

We invite contributions tackling these and other challenges related to advancing hydrological modeling and prediction using large-domain datasets and models. Potential submissions include but are not limited to:

  • Development and synthesis of large-domain meteorological and hydrological datasets.
  • Uncertainty quantification of datasets and error propagation in hydrological models. 
  • Novel application of large-domain datasets in model preparation, calibration, assimilation, forecast, and prediction.
  • The use of probabilistic/ensemble datasets and/or multi-model ensembles in hydrological modeling and prediction. 
Index Terms:

1805 Computational hydrology [HYDROLOGY]
1816 Estimation and forecasting [HYDROLOGY]
1840 Hydrometeorology [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]

Primary Convener:  Hongli Liu, University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada
Conveners:  Guoqiang Tang1, Martyn P Clark1 and Andy Wood2, (1)University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada(2)National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States
 
LamaH-Ice: Large-Sample Data for Hydrology and Environmental Sciences for Iceland (1046388)
Hordur Bragi Helgason, University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle WA, United States; National Power Company of Iceland, Reykjavík, Iceland and Bart Nijssen, University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States
 
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) (1047242)
Juliane Mai1, Hongren Shen1, Bryan Tolson1, Etienne Gaborit2, Richard Arsenault3, James R Craig1, Vincent Fortin4, Lauren Fry5, Martin Gauch6, Daniel Klotz6, Frederik Kratzert6, Nicole O'Brien7, Daniel G. Princz8, Sinan Rasiya Koya9, Tirthankar Roy10, Frank Seglenieks11, Narayan Shrestha11, Andre Guy Temgoua12, Vincent Vionnet4 and Jonathan M Waddell13, (1)University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada, (2)Environment and Climate Change Canada, Environmental Numerical Prediction Research, Dorval, QC, Canada, (3)École de technologie supérieure, Longueuil, QC, Canada, (4)Environment and Climate Change Canada, Meteorological Research Division, Dorval, QC, Canada, (5)NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI, United States, (6)Johannes Kepler University, Institute for Machine Learning, Linz, Austria, (7)Environment and Climate Change Canada, Burlington, Canada, (8)Environment and Climate Change Canada, National Hydrology Research Centre, Saskatoon, SK, Canada, (9)University of Nebraska-Lincoln, Civil and Environmental Engineering, Omaha, NE, United States, (10)University of Nebraska Lincoln, Civil and Environmental Engineering, Omaha, United States, (11)Environment and Climate Change Canada, National Hydrological Services, Burlington, ON, Canada, (12)Environment and Climate Change Canada, National Hydrological Services, Burlington, Canada, (13)US Army Corps of Engineers, Detroit, MI, United States
 
The Mesoscale Hydrologic Model at the Global Scale (1049540)
Oldrich Rakovec1, Rohini Kumar2, Pallav Kumar Shrestha3, Sebastian Muller1, Stephan Thober3 and Luis Samaniego2, (1)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (2)Helmholtz Centre for Environmental Research GmbH – UFZ, Computational Hydrosystems, Leipzig, Germany, (3)Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany
 
How much do climate and hydrograph uncertainty limit the performance of a perfect hydrologic model? (1051860)
C Rhett Rhett Jackson, University of Georgia, Warnell School of Forestry and Natural Resources, Athens, United States, Jacob Lafontaine, USGS, Atlanta, GA, United States and Kellie B Vache, Oregon State University, Corvallis, OR, United States
 
Estimating Daily Precipitation Climatology by Post-processing High-resolution Reanalysis Data (1054056)
Yiliang Du1, Q. J. WANG1, Chun-Hsu Su2, Wenyan Wu1 and Qichun Yang1, (1)The University of Melbourne, Parkville, Australia, (2)Bureau of Meteorology, Docklands, Australia
 
A Multi-Model Analysis of Streamflow Forecasting a Sub-Seasonal Horizon of a Large Watershed in Southern Brazil (1065888)
Cassia Paranhos, Companhia Paranaense de Energia, Curitiba, PARANA, BRAZIL, Camila Freitas, Companhia Paranaense de Energia Elétrica - COPEL, Curitiba, Brazil, Fernando M Fan, UFRGS Federal University of Rio Grande do Sul, Institute of Hydraulic Research, Porto Alegre, Brazil and Reinaldo Silveira, Sistema Meteorológico do Paraná - Simepar, Curitiba - Pr, Brazil
 
Improvements to snowmelt rate and timing using remotely sensed snow albedo and numerical weather prediction solar radiation data in a spatially distributed process-based snow model in the East River Watershed, Colorado, USA (1072434)
Joachim Meyer, University of Utah, Geography, Salt Lake City, UT, United States, McKenzie Skiles, University of Utah, Geography, Salt Lake City, United States and Christian Roland Steger, Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
 
A Benchmark for Probabilistic Seasonal Streamflow Forecasting over North America (1075797)
Louise Arnal1, Martyn P Clark2, Vincent Fortin3, Alain Pietroniro4, Vincent Vionnet3, Paul H Whitfield5 and Andy Wood6, (1)University of Saskatchewan, Centre for Hydrology, Canmore, Canada, (2)University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada, (3)Environment and Climate Change Canada, Meteorological Research Division, Dorval, QC, Canada, (4)University of Calgary, Department of Civil Engineering, Calgary, AB, Canada, (5)University of Saskatchewan, Centre for Hydrology, Canmore, AB, Canada, (6)National Center for Atmospheric Research, Hydrometeorological Applications Program, Research Applications Laboratory, Boulder, United States
 
Precipitation Data Fusion of Large Heterogeneous Datasets with Multiscale Kalman Smoother-based Scheme via CyberWater Framework (1077172)
Yihan Wang, University of Pittsburgh, Department of Civil and Environmental Engineering, Pittsburgh, PA, United States and Xu Liang, University of Pittsburgh, Department of Civil and Environmental Engineering, Pittsburgh, United States
 
Imposing Snow and Discharge Constraints on a Regional High-Resolution Precipitation Data Using Bayesian Inference (1079651)
Alejandro N Flores1, William J Rudisill2, Allison Nicole Vincent2, Jim P McNamara3, Rosemary W H Carroll4 and Hans-Peter Marshall1, (1)Boise State University, Department of Geosciences, Boise, ID, United States, (2)Boise State University, Boise, ID, United States, (3)Boise State Univ, Boise, United States, (4)Desert Research Institute Reno, Reno, NV, United States
 
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies (1080198)
Ziyun Yin1, Peirong Lin1, George H. Allen2 and Ryan Riggs3, (1)Peking University, Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Beijing, China, (2)Virginia Polytechnic Institute and State University, Department of Geosciences, Blacksburg, United States, (3)Texas A&M University, Department of Geography, College Station, United States
 
Looking back into the history of floods in Japan using a newly developed long-term gridded precipitation dataset (1085067)
Misako Hatono, Hiroshima University, Higashi-Hiroshima, Japan, Masashi Kiguchi, The Univ. of Tokyo, Tokyo, Japan, Kei Yoshimura, The University of Tokyo, Institute of Industrial Science, Kashiwa, Japan, Shinjiro Kanae, Tokyo Institute of Technology, Department of Civil and Environmental Engineering, Tokyo, Japan, Koichiro Kuraji, The University of Tokyo, The University of Tokyo Forests, Tokyo, Japan and Taikan Oki, The University of Tokyo, Graduate School of Engineering, Tokyo, Japan
 
The Global Hydro Intelligence Near-Real-Time system (1088419)
Yeosang Yoon1, Sujay V Kumar1, Jerry Wegiel1, Augusto Getirana1, Eric M Kemp1, Ahmad A Tavakoly2, Nicholas Olsen2, Kimberly McCormack3 and Heather K Levin4, (1)NASA Goddard Space Flight Center, Greenbelt, United States, (2)US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States, (3)National Geospatial Intelligence Agency, Office of Geomatics, St. Louis, MO, United States, (4)National Geospatial Intelligence Agency, Office of Geomatics, Saint Louis, United States
 
Insights from Dayflow: A Spatiotemporally Continuous Historical Streamflow Reanalysis Dataset for the Conterminous United States (1088655)
Ganesh R Ghimire, Carly Hansen, Sudershan Gangrade and Shih-Chieh Kao, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States
 
Investigating Predictability of Seasonal Streamflow Hindcasts From Physical and Machine Learning Models Using Ensemble Prediction Framework (1094865)
Parthkumar Modi, University of Colorado at Boulder, Department of Civil, Environmental and Architectural Engineering, Boulder, United States, Joseph R Kasprzyk, University of Colorado Boulder, Civil Environmental & Architectural Engineering, Boulder, CO, United States, Eric E Small, University of Colorado at Boulder, Department of Geological Sciences, Boulder, United States and Ben Livneh, Western Water Assessment, CIRES, University of Colorado, Boulder, United States
 
Evaluation of Parametric Uncertainty in Future Runoff Projection (1095821)
Donghui Xu1, Gautam Bisht1 and Tian Zhou1,2, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Laboratory, Richland, United States
 
Experimental Seasonal Water Supply Forecast in the California-Nevada Region (1099297)
Ming Pan1, Mu Xiao2, Qian Cao3, Robert Hartman4, Michael J Deflorio5, Agniv Sengupta2, Luca Delle Monache5, Alexander Gershunov6, Tamara Shulgina3 and Julie Kalansky1, (1)Scripps Institution of Oceanography, La Jolla, CA, United States, (2)Scripps Institution of Oceanography, La Jolla, United States, (3)University of California San Diego, La Jolla, United States, (4)Robert K. Hartman Consulting Associates, Roseville, United States, (5)Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United States, (6)University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States
 
Multimodel-based hydrological sensitivity analysis in Indian river basins (1102195)
Anuj Prakash Kushwaha II, Indian Institute of Technology Gandhinagar, Ahmedabad, 382, India and Vimal Mishra, Indian Institute of Technology Gandhinagar, Department of Civil Engineering, Ahmedabad, India
 
Quantifying the Impacts of Climate Change on the Hydrology of the Great Lakes Region (1107348)
Phyllis Feldpausch, Huy Dang and Yadu Pokhrel, Michigan State University, Department of Civil and Environmental Engineering, East Lansing, United States
 
Development of a Global Representative Hillslope Dataset for Earth System Models (1112830)
David M Lawrence and Sean C Swenson, National Center for Atmospheric Research, Boulder, CO, United States
 
PREDICTING THE UNPREDICTABLE: ADVANCES IN LARGE-DOMAIN HYDROLOGIC MODELING (Invited) (1113532)
Tricia Anne Stadnyk, University of Calgary, Calgary, AB, Canada
 
LEARNING FROM HYDROLOGIC MODEL FAILURE: A CASE FROM PHASE 1 OF THE NELSON-MiP PROJECT (1114741)
Tricia Anne Stadnyk1, Mohamed Ismaiel Ahmed2, Alain Pietroniro3, Oyémonbadé Hervé Rodrigue Awoye4, Ajay Ratna Bajracharya5, Juliane Mai6, Bryan Tolson7, Hongren Shen7, James R Craig7, Kristina A Koenig8, Shane Wruth8, Phillip Slota9, Mark Gervais9, Kevin Sagan9, Rajtantra Lilhare10, Stephen Déry10, Scott Pokorny11, Hank Venema12, Ameer Muhammad13, Curtis Hallborg13 and Mahkameh Taheri7, (1)University of Calgary, Calgary, AB, Canada, (2)University of Calgary, Department of Geography, Calgary, Canada, (3)University of Calgary, Department of Civil Engineering, Calgary, AB, Canada, (4)University of Manitoba, Department of Civil Engineering, Winnipeg, MB, Canada, (5)University of Calgary, Department of Geography, Calgary, AB, Canada, (6)Helmholtz Centre for Environmental Research UFZ Leipzig, Leipzig, Germany, (7)University of Waterloo, Department of Civil and Environmental Engineering, Waterloo, ON, Canada, (8)Manitoba Hydro, Winnipeg, MB, Canada, (9)Manitoba Hydro, Winnipeg, Canada, (10)University of Northern British Columbia, Prince George, BC, Canada, (11)University of Manitoba, Winnipeg, MB, Canada, (12)Strategic Consulting, Winnipeg, Canada, (13)Water Security Agency, Moose Jaw, SK, Canada
 
CAMELS-spat: Catchment Data for Spatially Distributed Large-Sample Hydrology (1117894)
Wouter Knoben, University of Saskatchewan, Centre for Hydrology, Canmore, AB, Canada and Martyn P Clark, University of Saskatchewan, Centre for Hydrology, Saskatoon, SK, Canada
 
Validation of TDX-Hydro; a global, TanDEM-X derived, 12m resolution hydrographic data suite (1119749)
Kimberly A McCormack1, Heather K Levin1, Amy L Morris1, Jaimeson G Fredericks1, Vincent Huening1, Ahmad Tavakoly2, Nicholas Olsen2, Yeosang Yoon3,4, Sujay V Kumar3, Jerry Wegiel3,4, Augusto Getirana3,4 and Eric M Kemp3,5, (1)National Geospatial Intelligence Agency, Office of Geomatics, Saint Louis, United States, (2)US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States, (3)NASA Goddard Space Flight Center, Greenbelt, United States, (4)Science Applications International Corporation, Reston, United States, (5)Science Systems and Applications, Inc., Lanham, United States
 
Computationally-Derived Global River Network Quality: Influence of User Decisions and Environmental Factors (1121426)
Nicholas Olsen, U.S. Army Corps of Engineers, Coastal and Hydraulics Laboratory, Vicksburg, United States, Ahmad A Tavakoly, US Army Corps of Engineers, Jacksonville, FL, United States, Kimberly McCormack, National Geospatial Intelligence Agency, Office of Geomatics, St. Louis, MO, United States and Heather K Levin, National Geospatial Intelligence Agency, Office of Geomatics, Saint Louis, United States
 
Developments in continental-scale, integrated hydrologic modeling: ParFlow-CONUS version 2.0 (1126277)
Danielle Tijerina1, Hoang Tran2, Chen Yang3, Jun Zhang4, Jackson Swilley1, Elena Leonarduzzi5, Ming Pan6,7, Laura E Condon4 and Reed M Maxwell8, (1)Princeton University, Civil & Environmental Engineering, Princeton, United States, (2)Pacific Northwest National Laboratory, Richland, United States, (3)Princeton University, Civil & Environmental Engineering, Princeton, NJ, United States, (4)University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States, (5)Princeton University, High Meadows Environmental Institute, Princeton, NJ, United States, (6)Center for Western Weather and Water Extremes, San Diego, United States, (7)Scripps Institution of Oceanography, UC San Diego, Center For Western Weather and Water Extremes, San Diego, United States, (8)Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States
 
A hydrological dataset for water resources applications in Chile (1131985)
Pablo A. Mendoza1, Nicolas Vasquez1, Eduardo Muñoz-Castro1, Nicolas Cortes1, Catalina Silva1, Alvaro Martinez-Conde1, Miguel Lagos-Zúñiga2, Naoki Mizukami3 and Ximena Vargas1, (1)Universidad de Chile, Department of Civil Engineering, Santiago, Chile, (2)Universidad de Chile, Advanced Mining Technology Center, Santiago, Chile, (3)National Center for Atmospheric Research, Boulder, United States
 
Attention-based Model for Rainfall-runoff Modeling Using Large-domain Datasets (1138116)
Jinyang Li1, Kuo-lin Hsu1, Ai-Ling Jiang2 and Soroosh Sorooshian3, (1)University of California Irvine, Department of Civil and Environmental Engineering, Irvine, CA, United States, (2)University of California, Irvine, Civil and Environmental Engineering, Irvine, CA, United States, (3)Univ California Irvine, Irvine, United States
 
Sources of Uncertainty for Hydrological Prediction with WRF-Hydro over the Snow-Covered Region in the Upper Indus Basin, Pakistan (1142182)
Muhammad Shafqat Shafqat Mehboob and Yeonjoo Kim, Yonsei University, Department of Civil and Environmental Engineering, Seoul, Korea, Republic of (South)
 
Hydrological Ensemble Approach for Flash Flood Early Warning System Development (1145561)
Joško Trošelj, National Institute for Environmental Studies, Tsukuba, Japan; Hiroshima University, Higashihiroshima, Japan
 
Benchmarking Pan-Arctic CMIP6 Model Streamflow to Observations: Understanding Utility across Different Spatial and Temporal Scales (1153843)
Kurt Solander, Los Alamos National Laboratory, Los Alamos, United States, Tian Zhou, Pacific Northwest National Laboratory, Richland, United States, Katrina E Bennett, Los Alamos National Lab, Los Alamos, NM, United States and Jonathon P Schwenk, Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, United States
 
Local and regional evaluation of a hyper-resolution continental hydrological simulation of West Africa (1158285)
Jean-Martial Cohard, Univ. Grenoble Alpes, Grenoble, France, Thierry Pellarin, CNRS, The Institute for Geosciences and Environmental research (IGE), Grenoble, France, Basile Hector, UGA, CNRS, Institut des Geosciences de l’Environnement (IGE) UMR 5001, IGE, Grenoble, France, Alban Depeyre, Univ. Grenoble Alpes, The Institute for Geosciences and Environmental research (IGE), Grenoble, France, Laura E Condon, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Reed M Maxwell, Princeton University, Civil & Environmental Engineering, High Meadows Environmental Institute, Princeton, United States
 
Developing a High Resolution Model of Historic (1990-2020) Alaskan River Temperatures (1159712)
Dylan Blaskey, University of Colorado - Boulder, Boulder, CO, United States, Joshua Koch, USGS Alaska Science Center, Anchorage, United States, Michael N Gooseff, University of Colorado Boulder, Civil, Environmental, and Architectural Engineering, Boulder, United States, Yifan Cheng, National Center for Atmospheric Research, Boulder, CO, United States, Andrew James Newman, National Center for Atmospheric Research, Boulder, United States and Keith N Musselman, University of Colorado at Boulder, Institute of Arctic and Alpine Research, Boulder, United States
 
Multi-Variate Hydrological Model Calibration Strategy Selection Based on Diverse Sources of Uncertainty (1160962)
Saumya Srivastava, Indian Institute of Science, Bangalore, Bangalore, India and D. Nagesh Kumar, Indian Institute of Science, Department of Civil Engineering, Bangalore, India
 
Impact of Subjective Modelling Decisions on Simulation of Flood Events: A Case Study on the Indian Catchment (1161021)
Ashlin Alexander, Indian Institute of Science, Bangalore, Bangalore, India and D. Nagesh Kumar, Indian Institute of Science, Department of Civil Engineering, Bangalore, India
 
Challenges and Successes in Building a Global, Operational, Physics-based Machine Learning Model (1161152)
Laura K Read, Alden Keefe Sampson, David Lambl and Eliza Hale, Upstream Tech, Alameda, United States
 
Towards the Design of Large-scale Hydrologic Knowledge Graphs for Data Integration, Management, and Analytics for data-driven models (1163262)
Hasnat Aslam Mr, University of Nebraska Lincoln, Lincoln, NE, United States and Francisco Munoz-Arriola, Department of Biological Systems Engineering & School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
 
Independent, identically distributed residual errors of watershed models enable uncertainty evaluation and risk-based management (Invited) (1164357)
William Hastings Farmer, US Geological Survey, Lakewood, United States and Richard M Vogel, Tufts University, Department of Civil and Environmental Engineering, Medford, MA, United States
 
A Shift in Paradigm from Regional to Global Flood Frequency Analysis (1164551)
Nischal Kafle1, Francesco Dell'Aira1, Antonino Cancelliere2 and Claudio I Meier3, (1)University of Memphis, Memphis, TN, United States, (2)University of Catania, Catania, Italy, (3)University of Memphis, Department of Civil Engineering, Memphis, TN, United States
 
Impact of Climate Change on River Flow Characteristics in the Western Himalayas (1166380)
Vivek Gupta, Indian Institute of Technology Mandi, School of Civil and Environmental Engineering, Mandi, India
 
Groundwater Storage Variability in Headwater Catchments of the Upper Colorado River Basin: Improving Our Understanding of Why Runoff in Mountain Environments is Highly Variable (1184319)
Meg Wolf1, Logan Jamison2, Courtenay Strong3 and Paul D Brooks1, (1)University of Utah, Geology and Geophysics, Salt Lake City, UT, United States, (2)University of Utah, Salt Lake City, United States, (3)University of Utah, Atmospheric Sciences, Salt Lake City, UT, United States
 
Development of a High-Resolution Nationwide Distributed Rainfall-Runoff Model for Flash Flood Predictions (1188752)
Takahiro Sayama, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan
 
Are emerging Earth Observations inconsistent in mapping Global Surface Water Extents? (1190538)
Arushi Khare1, Adnan Rajib2, Qianjin Zheng1, Qiusheng Wu3, Heather E Golden4, Charles R. Lane5, Jay Christensen4, Travis A Dahl6, Jodi L. Ryder7 and Brian C. McFall8, (1)Texas A&M University Kingsville, Environmental Engineering, Kingsville, TX, United States, (2)Texas A&M University Kingsville, Environmental Engineering, Kingsville, United States, (3)University of Tennessee, Geography, Knoxville, United States, (4)US Environmental Protection Agency, Office of Research & Development, Cincinnati, United States, (5)US Environmental Protection Agency, Athens, United States, (6)US Army Corps of Engineers, ERDC-CHL, Vicksburg, United States, (7)US Army Corps of Engineers, Engineer Research and Development Center, Environmental Laboratory, Vicksburg, MS, United States, (8)US Army Corps of Engineers, ERDC CHL, Coastal Engineering Branch, Vicksburg, United States
 
How does soil moisture modulate snowmelt runoff generation? Insights into the past and the future (1193631)
Dongyue Li1, Yiwen Fang2, Manon von Kaenel3, Yufei Liu3, Steven A Margulis3 and Dennis P Lettenmaier4, (1)University of California Los Angeles, Los Angeles, CA, United States, (2)Graduate Student, Civil and Environmental Engineering, Los Angeles, United States, (3)UCLA, Department of Civil and Environmental Engineering, Los Angeles, United States, (4)UCLA, Department of Geography, Los Angeles, CA, United States
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