H23N:
Role of New Data Sets and Analysis Methods to Advance Hydrological Process Understanding at the Catchment Scale Posters


Submit an Abstract to this Session


Session ID#: 51011

Session Description:
Difficulties in the measurement of fluxes at catchment scale is one of the major reasons why catchment hydrology relies on mathematical modeling to a great extent, however, improper process understanding can lead to spurious model outcomes. It is also an established fact that the uncertainties at different stages of modeling play a major role in determining the usefulness of a particular model. With the advent of satellite-based remote sensing as well as comprehensive hydrological data repositories, it is now becoming increasingly more tractable to test novel hypotheses to help improve process understanding at larger scales. Furthermore, innovative ways of exploring the existing datasets also has the potential to provide new insights. Keeping these under consideration, we invite original contributions from the following two interrelated focus areas: (1) novel hypothesis testing utilizing new sources of hydrological data and (2) innovative ways to characterize and estimate hydrological fluxes at catchment scale.
Primary Convener:  Tirthankar Roy, University of Nebraska Lincoln, Lincoln, NE, United States
Conveners:  Antonio Alves Meira Neto, Colorado State University, Department of Civil and Environmental Engineering, Fort Collins, United States, Rodrigo Valdés-Pineda, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Paulo T. S. Oliveira, Federal University of Mato Grosso do Sul, Brasil, Campo Grande, Brazil
Primary Liaison:  Tirthankar Roy, University of Nebraska-Lincoln, Civil and Environmental Engineering, Lincoln, United States
Chairs:  Tirthankar Roy, University of Nebraska-Lincoln, Civil and Environmental Engineering, Lincoln, United States and Rodrigo Valdés-Pineda, University of Arizona, Hydrology and Atmospheric Sciences, Tucson, AZ, United States
OSPA Liaison:  Antonio Alves Meira Neto, Colorado State University, Department of Civil and Environmental Engineering, Fort Collins, United States

Cross-Listed:
  • EP - Earth and Planetary Surface Processes
Index Terms:

1804 Catchment [HYDROLOGY]
1836 Hydrological cycles and budgets [HYDROLOGY]
1874 Ungaged basins [HYDROLOGY]

Abstracts Submitted to this Session:

Seasonal variation of groundwater discharge using Sr and water isotopes in a watershed with extensive irrigated paddies (422709)
Takeo Yoshida1, Takanori Nakano2, Ki-Cheol Shin2, Takeo Tsuchihara3, Susumu Miyazu4 and Tomijiro Kubota3, (1)NIRE National Institute for Rural Engineering, Tsukuba, Japan, (2)Research Institute for Humanity and Nature, Kyoto, Japan, (3)Institute for Rural Engineering, NARO, Tsukuba, Japan, (4)NARO National Agriculture and Food Research Organization, Ibaraki, Japan
Advancing Process Understanding through Data-Intensive Science (Invited) (347457)
Christa D Peters-Lidard1, Dr. Grey Stephen Nearing, PHD2, David M Mocko3, Sujay V Kumar4, Youlong Xia5, Craig Pelissier6, James V Geiger7, Jules Kouatchou4 and Daniel Duffy8, (1)NASA GSFC, Greenbelt, United States, (2)Upstream Tech, Tuscaloosa, United States, (3)SSAI, Greenbelt, United States, (4)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (5)NUIST Nanjing University of Information Science and Technology, Nanjing, China, (6)NASA Goddard Space Flight Center, Computational and Information Science & Technology Office, Greenbelt, MD, United States, (7)Goddard Space Flight Center, Greenbelt, MD, United States, (8)NASA Center for Climate Simulation, Greenbelt, United States
Diagnostic-evaluation of a distributed model using hydrological signatures (391381)
Ivan Horner1, Flora Branger2, Olivier Vannier3 and Isabelle Braud1, (1)IRSTEA Lyon, Villeurbanne Cedex, France, (2)INRAE Lyon, RiverLy, Villeurbanne, France, (3)Compagnie National du Rhône, Lyon, France
Effects of Hurst-Kolmogorov Dynamics in Intensity-Duration-Frequency Curves (454941)
Tirthankar Roy1, Panayiotis Dimitriadis Sr2, Theano Iliopoulou3, Demetris Koutsoyiannis2 and Eric F Wood4, (1)University of Nebraska Lincoln, Lincoln, NE, United States, (2)National Technical University of Athens (NTUA), Marousi Athens, Greece, (3)National Technical University of Athens, Athens, Greece, (4)Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States
Long-term Experimental Watershed Infrastructure Facilitates Novel Advancements in Semiarid Catchment Hydrology through Multi-Agency Collaboration (437465)
Mark Anderson Kautz1, Dr. Joel A Biederman, PhD2, Menberu B Meles3, Claire Bunch4, Jay Cederberg5, Eleonora M. Demaria6, Brandon T Forbes7, Patrick N Gatlin8, David C Goodrich6, Jonathan J Gourley9, Pieter Hazenberg10, Phil Heilman6, Chandra Holifield Collins6, Mary Nichols6, Walter Arthur Petersen11, Russell L Scott12, Remko Uijlenhoet13, Christopher Smith14 and C Jason Williams15, (1)USDA-ARS, Tucson, United States, (2)USDA ARS, Pendleton, United States, (3)USDA-ARS, Sustainable Agricultural Water Systems, Davis, United States, (4)USGS Arizona Water Science Center, Tucson, United States, (5)USGS Arizona Water Science Center, Flagstaff, AZ, United States, (6)USDA-ARS, Southwest Watershed Research Center, Tucson, AZ, United States, (7)USGS Arizona Water Science Center, Tucson, AZ, United States, (8)NASA Marshall Space Flight Center, Huntsville, AL, United States, (9)NOAA/OAR National Severe Storms Laboratory, Oklahoma City, United States, (10)Unversity of Arizona, Tucson, AZ, United States, (11)NASA MSFC, Science Research and Projects Office, Huntsville, AL, United States, (12)USDA-ARS, Southwest Watershed Research Center, Tucson, United States, (13)Wageningen University and Research, Environmental Sciences, Wageningen, Netherlands, (14)University of Alaska Fairbanks, Geographic Information Network of Alaska, Fairbanks, AK, United States, (15)USDA-ARS Boise, Northwest Watershed Research Center, Boise, United States
Machine Learning with Aquatic DNA to Estimate Hydrologic and Catchment Characteristics (456537)
Dawn URycki, Oregon State University, Department of Biological & Ecological Engineering, Corvallis, United States, Stephen P Good, Oregon State University, Department of Biological and Ecological Engineering, Corvallis, OR, United States and Byron C Crump, Oregon State University, College of Earth, Ocean, and Atmospheric Science, Corvallis, United States
Scaling of flow quantiles and mean catchment fluxes and storage provides empirical formulation of the flow duration curve (373275)
David Litwin, Temple University, Earth and Environmental Science, Philadelphia, PA, United States, Antonio Alves Meira Neto, Colorado State University, Department of Civil and Environmental Engineering, Fort Collins, United States and Peter Troch, Biosphere 2, Tucson, AZ, United States
Testing the use of Drain Flow Measurements to guide Calibration and Improving Local Scale Model Performance in a Distributed Hydrological Catchment Model for a Danish Glacial Till Area (348582)
Ida Bjørnholt Karlsson, Geological Survey of Denmark and Greenland, København K, Denmark, Anker L Højberg, Geological Survey of Denmark and Greenland, Hydrology, København K, Denmark, Xin He, Geological Survey Denmark & Greenland, Copenhagen V, Denmark and Bo V. Iversen, Aarhus University, Department of Agroecology, Tjele, Denmark
The hydrologic pendulum: How information-theory can complement machine learning and physically-based approaches for discharge prediction (426163)
Hongxu Ma1, Dino G. Bellugi2, Laurel Larsen3 and Christopher Tennant3, (1)Google, Mountain View, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)University of California Berkeley, Geography, Berkeley, CA, United States
Upland Microtopography and Implications to Surface Water Detention in Maine (450414)
Bea Elizabeth Van Dam1,2, Sean MC Smith1,2, Kate Beard2,3 and Samuel G. Roy2,4, (1)University of Maine, School of Earth and Climate Sciences, Orono, ME, United States, (2)Sen. George J. Mitchell Center for Sustainability Solutions, Orono, ME, United States, (3)University of Maine, School of Computing and Information Science, Orono, ME, United States, (4)University of Maine, Orono, ME, United States
What Influences Flood Generating Processes Around the World? (377829)
Lina Stein1, Francesca Pianosi2, Wouter Berghuijs3 and Ross A Woods2, (1)University of Bristol, Civil Engineering, Bristol, United Kingdom, (2)University of Bristol, School of Civil, Aerospace and Design Engineering, Bristol, United Kingdom, (3)ETH Zurich, Department of Environmental Systems Science, Zurich, Switzerland

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