Francisco Muñoz-Arriola

Francisco Muñoz-Arriola

Assistant Professor of Hydroinformatics and Integrated Hydrology
Academic Degrees
  • Ph.D., Civil and Environmental Engineering, Duke University
  • M.S., Coastal Oceanography, Universidad Autónoma de Baja California
  • B.S., Oceanography, Universidad Autónoma de Baja California
Graduate and Undergraduate Research Opportunities
  • 60% Research
  • 40% Teaching
Areas of Research and Professional Interest
  • Data Science
  • Integrated Hydrology (nexus water quality-quantity)
  • Resilient Complex Systems
  • Predictability of Hydrometeorological and Climate Extremes
  • Nexus Water-Food-Energy-Ecosystems Services in a Changing Environment
  • Phenotype predictability
  • Global Water System
Teaching Interests
  • Predictability of Hydrometeorological and Climate Extremes
  • Complex Systems Modeling
  • Attribution Science and Decision Making
  • Global Water System: Science and Engineering
Courses Taught
  • Soil and Water Resources Engineering
  • Hydroclimatology
  • Small Watershed Hydrologic Modeling
  • Physical Hydrology
  • Seminar I
  • Statistics of Extreme Events in Water Resources
Honors and Awards
  • Robert B. Daugherty Water for Food Institute Fellow
  • National Science Foundation-Enabling the Next Generation of Hazards and Disasters Researchers
  • American Meteorological Society/National Science Foundation-Summer Policy Colloquium

Selected Publications
  • ShaikR., F. Munoz-Arriola, D. A. Rico, and S. L. Bartelt-Hunt (2019). Modelling Water Temperature’s Sensitivity to Atmospheric Warming and River Flow. In Environmental Biotechnology: for sustainable future (Eds. R. B. Sobti, N. Arora, and R. Kothari) ISBN 978-981-10-7283-3.
  • Ou, G., F. Munoz-Arriola, D. Uden, D. Martin and C. Allen (2018). Groundwater Availability in a Changing Climate: The case of Irrigated Landscapes in Geopolitically Contentious Areas. Climatic Change.

  • Amaranto, A., F. Munoz-Arriola, G. Meyer, D. Solomatine, and G. Corzo (2018). Semi-seasonalPredictability of Water-table Changes Using Machine Learning Methods in Response to Integrated Hydroclimatic and Management Controls. Journal of Hydroinformatics.

  • Uden, D.R., C.R. Allen, F. Munoz-Arriola, G. Ou, and N. Shank (2018). A Framework for Tracing Social–Ecological Trajectories and Traps in Intensive Agricultural Landscapes. Sustainability..

  • Rudnick, D.R., T. Lo, J. Singh1, R. Werle, F. Muñoz-Arriola,  T.M. Shaver, C.A. Burr, and T.J. Dorr (2018). Reply to comments on "Performance assessment of factory and field calibrations for electromagnetic sensors in a loam soil". 203:272-276. DOI:10.1016/j.agwat.2018.02.036.
  • Singh1, J., T. Lo, D.R. Rudnick, T.J. Dorr, C.A. Burr, R. Werle, T.M. Shaver, and F. Muñoz-Arriola (2018). Performance Assessment of Factory and Field Calibrations for Electromagnetic Sensors in a Loam Soil. Agricultural Water Management.196: 87-98.

  • Lawrence-Dill, C.J., Patrick Schnable, Nathan Springer, and: Natalia de Leon, Jode Edwards, David Ertl, Shawn Kaeppler, Nick Lauter, John McKay, Francisco Munoz-Arriola, Seth Murray, Duke Pauli, Nathalia Penna Cruzato, Colby Ratcliff, James Schnable, Kevin Silverstein, Edgar P. Spalding, Addie Thompson, Ruth Wagner, Jason Wallace, Justin Walley, and Jianming Yu (2018). White paper: High Throughput, Field-Based Phenotyping Technologies for the Genomes to Fields (G2F) Initiative. 2018 NIFA FACT Workshop. January 28-30, 2018, 8 pp.
  • Das, A., F. Munoz-Arriola, S. Singh, and M. Kumar3(2017).Nutrient Dynamics of Brahmaputra (Tropical River) during Monsoon Period. Desalinization and Water Treatment.doi:10.5004/dwt.2017.20788.

  • Shekhar, S., J. Colleti, F. Munoz-Arriola, L. Ramaswamy, C. Krinz, L. Varshney, D. Richardson (2017). Intelligent Infrastructure for Smart Agriculture: An Integrated Food, Energy and Water System. eprint arXiv:1705.01993. 2017arXiv170501993S. A Computing Community Consortium (CCC) white paper, 8 pp. 

  • Avery, W.A., C. Finkenbiner, T. E. Franz, T. Wang, A. L. Nguy-Robertson, A. Suyker, and T. Arkebauer, and F. Munoz-Arriola (2016).Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content. Hydrol. Earth Syst. Sci., 20, 3859–3872.

  • Livneh, B., T. Bohn, D. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, D. Cayan, and L. Brekke (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and southern Canada 1950-2013. Nature - Scientific Data, doi:10.1038/sdata.2015.42.
  • Munoz-Arriola, F., D. Martin, and D. Eisenhauer (2014). Nebraska’s Water Resources in Changing Climate. In: Understanding and Assessing Climate Change: Implications for Nebraska.
  • Perez-Morga, N., T. Kretzshmar, T. Cavazos, S. Smith, and F. Munoz-Arriola (2013).Variability of Extreme Precipitation in coastal River Basins of the Southern Mexican Pacific Region. Geofisica Internacional. 52(3): 277-291.
  • Frans, C., E. Istanbulluoglu, M. Vimal, F. Munoz-Arriola, and D.P. Lettenmaier (2013). On runoff trends in the Upper Mississippi River Basin: influences of climate and land use. Geophysical Research Letters. 40, doi:10.1002 /grl.50262, 2013.
  • Wilder, M., G. Garfin, P. Ganster, H. Eakin, P. Romero-Lankao, F. Lara-Valencia, A. Cortez- Lara, S. Mumme, C. Neri, and F. Munoz-Arriola (2013). Impacts of Future Climate Change in the Southwest on Border Communities. In: National Climate Assessment Southwest.
  • Tang, Q., E. Vivoni, F. Munoz-Arriola, and D. P. Lettenmaier (2012). Predictability of evapotranspiration patterns using remotely-sensed vegetation dynamics during the North American monsoon. Journal of Hydrometeorology, 13(1), 103-121.