Francisco Muñoz-Arriola

Hydroinformatics and Integrated Hydroclimate research Group

NASA GPM mission

Associate 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

Appointment

  • 60% Research
  • 40% Teaching

Curriculum Vitae (CV):

Areas of Research and Professional Interest

  • Data science
  • Integrated hydrology (water quality, quantity, and ecosystem resilience)
  • Surface water and groundwater conjunctive use, interactions, and integration
  • Remote and proximal sensing applications
  • Coupled natural-human systems
  • Complex systems
  • Predictability of hydrometeorological and climate extremes
  • Climate-resilient infrastructure
  • Phenotype predictability
Teaching Interests
  • Predictability of Hydrometeorological and Climate Extremes
  • Complex Systems Modeling
  • Attribution Science and Decision Making
  • Design and Management of Resilient Water Infrastructure

Courses Taught

Research Group's News

Ashish Kumar Ph.D. student in our Hydroinformatics and Integrated Hydroclimate Lab and in the Civil Engineering Department at the Indian Institute of Technology Bombay successfuly defend his doctoral degree

Ashish – Ph.D. student in the Civil Engineering Department at the Indian Institute of Technology Bombay-- presented his dissertation "Machine Learning Approach for Improving Near-Real-Time Satellite Rainfall Estimates and Streamflow Simulations." Ashish was co-advised by Professor RAAJ Ramsankaran and Francisco Munoz-Arriola. Ashish published two articles. In the first article "Machine learning approach for improving near-real-time satellite-based rainfall estimates by integrating soil moistureKumar et al., Remote Sensing  (2019), Ashis merges rainfall and soil moisture data from remote sensing, improving rainfall estimates during the monsoon season in Central India. In his second paper "A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchmentKumar et al. Journal of Hydrology (2021), Ashish used a machine learning technique to improve the estimations of streamflow. Ashish will join the Indian Institute of Technology Delhi, where he will continue his work integrating remote sensing data and data-driven models.

CALL FOR CHAPTERS- Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation

Faculty from four continents create a collection of basic and applied research products to apply remote sensing to precision agriculture. Remote sensing technology is playing an increasingly important role in agriculture by providing timely spectral information. This information can be used to assess the biophysical indicators of plant health, quantify crop yield, map the soil quality, and improve agroecological forecasts. This book will compile the latest applications of Remote Sensing in agriculture using spaceborne, airborne, and drones’ geospatial data through the growing-season continuum. We invite the community to submit their chapter proposals no later than March 31, 2021. Click the link above for more information.

About Francisco Muñoz-Arriola

I am an Associate Professor in Hydroinformatics and Integrated Hydroclimate in the Department of Biological Systems Engineering and the School of Natural Resources. My academic and professional experiences encompass diagnostics and sub-seasonal to seasonal prognostics of extreme hydrometeorological and climate events, and their effects on infrastructure (i.e., water resources, agriculture, and ecosystem services). 

In the natural and built environment, for example, water quality and quantity are components of a complex system regulated or exacerbated by extreme hydrometeorological and climate events (EHCEs), fluctuating markets, technological developments, social behaviors, and evolving policies and decision making. Thus, the (re)design and management of resilient water infrastructure in a non-stationary world demands a better understanding of the underlying principles that enable water to maintain their core functions across geospatial attributions and management scales.

Honors and Awards

  • University of Nebraska, College of Law  Nebraska Governance and Technology Center
  • University of Nebraska Public Policy Faculty Fellow
  • Robert B. Daugherty Water for Food Institute Faculty Fellow
  • University of Nebraska-Lincoln, College of Engineering - Annual Recognition Teaching Award, Recipient (2019)
  • University of Nebraska-Lincoln-Inclusion and Diversity Faculty fellow, (2018-now)
  • University of Nebraska-Lincoln, College of Engineering Research - Annual Recognition Award, Recipient (2018)
  • National Science Foundation-Interdisciplinary Methods (for Disaster Research), Fellow, Recipient (2015-16)
  • National Science Foundation-Enabling the Next Generation of Hazards and Disasters Researchers Fellow, Recipient (2015-16)
  • University of Nebraska-Lincoln, Parent Association and Teaching Council-Contributions to Students Award, Recipient (2015)
  • America Meteorological Society/National Science Foundation-Summer Policy Colloquium Fellow, Recipient (2014)
  • Dougherty Water for Food Global Institute, Faculty Fellow, Recipient (since 2014)
  • University of Nebraska-Lincoln-Layman Award, Recipient (2014)
  • University of Nebraska-Lincoln-Research Development Fellows Program, Recipient (2013-2014)

Funding

  • University of Nebraska-Lincoln-Agriculture Research Division and Daugherty Water for Food Global Institute (2019-2020). Irrigation Sustainability. PI, $40,000.
  • University of Nebraska-Lincoln-Agriculture Research Division (2019-2020). Predictability and resilience of groundwater systems in Nebraska: coupling natural-human systems. PI, $30,000.
  • United States Department of Agriculture-NIFA Foundational’s Plant Breeding for Agricultural Production (2018-2021). Gene-to-Global Hydroclimatic Controls on Hybrid Performance Forecast. PI, $490,000.
  • National Science Foundation-Research Training (NRT) (2017-2022). Training in Theory and Application of Cross-scale Resilience in Agriculturally Dominated Social Ecological Systems. One of five Co-PIs, $3,000,000.
  • University of Nebraska-System Science (2017-2018). Modeling Resilient Interdependent Systems for Data-driven Decision Support. Co-PI, $20,000.
  • US Geological Survey 104B (2017). Impact of Variable Rate Irrigation on Consumptive Use of Water Resources. Co-PI, $20,000.
  • University of Nebraska-Lincoln-Office of Research and Economic Development (2016-2017). Dynamics and Trade-offs among Social, Economic, and Ecological Components of Resilience in Working Agricultural Landscapes. Co-PI, $100,000.
  • United States Department of Agriculture-Foundational-HATCH Project (2016-2020). Predictability of Water Distribution and Transport across Spatial and Temporal Scales: An Application on Cropland Management. PI-Four Co-PIs (Approved - No funds provided and valued in $3,200,000).

Selected Publications

  • Muñoz-Arriola, Francisco; Abdel-Monem, Tarik; Amaranto, Alessandro. 2021. "Common Pool Resource Management: Assessing Water Resources Planning for Hydrologically Connected Surface and Groundwater Systems" Hydrology 8, no. 1: 51. https://doi.org/10.3390/hydrology8010051.
  • Sarzaeim1, P., W. Ou1, L. Alves de Oliveira2, and F. Munoz-Arriola (Accepted). Spatiotemporal diagnostics and prognostics of major crops’ vulnerabilities to flooding in the Northern High Plains. ASCE’s Proceedings of GEO-Extreme 2021.
  • Wilson, A., R. Cielli, F. Munoz-Arriola, T. Parzybok, J. Giobannetone, J. Vano and M. Dettinger (Accepted). Toward Building Infrastructure Resiliency to Future Hydroclimate Extremes: A Case Study Investigation. ASCE’s Proceedings of GEO-Extreme 2021.
  • Ashish Kumar 1, RAAJ Ramsankaran 3, Luca Brocca, and Francisco Munoz-Arriola (2021). Expanding Machine learning modeling for improving near-real-time satellite-based rainfall-runoff forecasts in India . Journal of Hydrology.
  • Pandey, V., P. K. Srivastava, R. K. Mall, F. Munoz-Arriola, and D. Han (2020). Multi-Satellite Precipitation Products for Meteorological Drought Assessment and Forecasting in Bundelkhand region of Central India. Geocarto Internacional.  https://doi.org/10.1080/10106049.2020.1801862.
  • Amaranto, A., F. Pianosi, D. Solomatine, G. Corzo-Perez, and F. Munoz-Arriola (2020). A Sensitivity Analysis of Hydroclimatic Controls of Data-driven Groundwater Forecast in Irrigated Croplands. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124957
  • Ashish Kumar, RAAJ Ramsankaran, Luca Brocca, Francisco Munoz-Arriola (2019). A Machine learning approach for improving near-real-time satellite-based rainfall estimates by integrating soil moisture. Remote Sensing. doi:10.3390/rs11192221
  • Amaranto, A., F. Munoz-Arriola, D. Solomatine, and G. Corzo (2019). A Spatially enhanced data-driven multi-model to improve semi-seasonal groundwater forecasts in the High Plains aquifer, USA.Water Resources Research.
  • Khan, M., F. Munoz-Arriola, R. Shaik, and P. Greer(2019). Spatial heterogeneity of temporal shifts in extreme precipitation across India. Journal of Climate Change. DOI: 10.3233/JCC190003.

  • 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..

For additional publications press here.

BOOKS

CHAPTERS

  • Janin, J., F. Munoz-Arriola, and D. Khare (Accepted). Short-Term Resilience and Transformation of Urban Socio-environmental Systems to COVID-19 Lockdowns in India using Air Quality as Proxy. In ENVIRONMENTAL RESILIENCE AND TRANSFORMATION IN TIMES OF COVID-19 (Eds. Ramanathan et al). Elsevier.

  • Shaik3 R., F. Munoz-Arriola, D. A. Rico1, 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.

  • 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.

  • 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. 

PROCEEDINGS

  • Rico, D.A.1, Carrick Detweiler, and Francisco Muñoz-Arriola (2020). Power-over-Tether UAS Leveraged for Nearly-Indefinite Meteorological Data Acquisition. 2020 ASABE Annual International Meeting, Paper No. 1345. DOI: https://doi.org/10.13031/aim.202001345.
  • Sarzaeim, P1., D. Jarquin, and F. Muñoz-Arriola (2020). Analytics for climate-uncertainty estimation and propagation in maize-phenotype predictions. 2020 ASABE Annual International Meeting, Paper No. 1165. DOI: https://doi.org/10.13031/aim.20884.
  • Garret Williams4, Parisa Sarzaeim1, and Francisco Muñoz-Arriola (2020). Simplification of Complex Environmental Variations on Maize-Phenotype Predictability. 2020 ASABE Annual International Meeting, Paper No. 1291. DOI: https://doi.org/10.13031/aim.201291.
  • Luciano Alves de Oliveira2, Bryan L Woodbury, Jarbas Honorio de Miranda, and Francisco Munoz-Arriola (2020). Geospatial upscaling of atrazine’s transport using electromagnetic induction across point to field scale. 2020 ASABE Annual International Meeting, Paper No. 884. DOI: https://doi.org/10.13031/aim.202001165.
  • Banda, M. M., D. M. Heeren, D. L. Martin, F. Munoz-Arriola, and L. G. Hayde (2019). Economic analysis of deficit irrigation in sugarcane farming: Nchalo Estate, Chikwawa District, Malawi. 2019 ASABE Annual International Meeting, Paper No. 1900852, Boston, Mass. 19 pages. 
  • Korus, J.T., K. Cameron, C.M. Hobza, N-P.Jensen, D. A. Rico, and F. Munoz-Arriola (2018). Integrating AEM and borehole data for regional hydrogeologic synthesis: tools and examples from Nebraska, USA. AEM 2018/7th International Workshop on Airborne Electromagnetics. June 20, 2018, Fjordvej, Denmark.
  • Munoz-Arriola, R. Shaik4, and M. Kahn2 (2017). Toward a Food-Energy-Water-Ecosystem Services Nexus for Rapid Growing Cities in a Changing Climate. International Symposium on Sustainable Urban Environment (ISSUE 2017). Tezpur University, Assam, 23-24 June 2017. 5pp.

SOFTWARE

  • Hohbein, H., A. Zhang, Z. Trautman, D. Brecic, J. Carter. P. Sarzaeim, D. Jarquin, B. Ramamurthy, and F. Munoz-Arriola (2020). Prototype of the GEnetics by ENvironment (GEEN): A Phenotype Predictive System.
  • Isaak Arslan4, Jake Field4, Cale Harms4, Hallie Hohbein4, Miracle Modey4, B. Ramamurthy, D. Benet. Y-C Chen, and F. Munoz-Arriola (2019). NEO-SAT: An information support system for flood-disaster management.
  • Cantú-Guerrero1, J., Craven, J., A. Amaranto1, G. Corzo-Perez, F. Munoz-Arriola (2018). Prototype of Software Platform to Forecast Semiseasonal Well-Level Responses to Climate and Irrigation Scheduling in the High Plains.
  • Herrera-Leon1, L. A., M. Khan1, G. Lopez-Morteo3, and F. Munoz-Arriola (2018). Unified-access mechanisms for Weather, Climate, Water data with geospatial constrains and resolutions.
  • Osornio-Hernandez1, J. D., G. Lopez-Morteo3, and F. Munoz-Arriola (2018). Database management for multi-dimensional data storage.

Recent Presentations

  • Carter, J. P. Sarzaeim, D. Jarquin, R. Quinones, E. Tanghanwaye, and F. Munoz-Arriola. The GEnetic by Environment (GEEN) Phenotype Predictive System Software Development. NAPPN Annual Conference. February 17, 2021.
  • Wilson, A. M., R. Cifelli, F. Munoz-Arriola, J. Giovannettone, J. Vano, T. Parzybok, A. Dufour, J. Jasperse, K. Mahoney, and B. McCormick. Efforts to Build Infrastructure Resiliency to Future Hydroclimate Extremes. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021.
  • Jain, J1., D. Khare, and F. Munoz-Arriola. Mapping Attributions between Flood Vulnerabilities and Risk Management Policies in India. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021.
  • Ntaganda5, P., M. Shyaka5, and F. Munoz-Arriola. Rwanda's Hydroclimate across Urban and Agricultural Landscapes. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021.
  • Wilson, A. M., R. Cifelli, F. Munoz-Arriola, J. Giovannettone, J. Vano, T. Parzybok, A. Dufour, J. Jasperse, K. Mahoney, and B. McCormick. Efforts to Build Infrastructure Resiliency to Future Hydroclimate Extremes. American Geophysical Union, Fall Conference, Virtually. December 9, 2020.
  • Ghosh, K3., F. Munoz-Arriola, and T. Chakraborty. The impact of river regulation on streamflow and sediment dynamics in the Eastern Himalayan river basin. The Geological Society of America 2020, October 27, 2020.
  • Jarquin4, D., F. Munoz-Arriola, P. Sarzaeim1, A. Amaranto1Improving genomic prediction of target hybrids in unobserved environments using geospatial assessment of predictive analytics derived from machine learning techniques. 6th International Conference of Quantitative Genetics. Virtual and On Demand, November 10-14, 2020.Ntaganda, P., L. Alves de Oliveira, F. Muñoz-Arriola. Groundwater data records and hydroclimate perspectives in the Northern High Plains. 2020 ASABE 2020 Annual International Meeting. Virtual and On Demand, July 13-15, 2020.
  • Munoz-Arriola, F., C. Wunderlin, P. Sarzaeim, M. Khan, W. Ou, and P. Greer. Toward the integration of hydrometeorological and climate complexities in standards for resilient infrastructure design. 100th American Meteorological Society Annual Meeting, Boston, MA. January 15, 2020.
  • Sarzaeim, P., W. Ou, Khan, L. Alves, and F. Munoz-Arriola. Spatiotemporal diagnostics of major crops’ vulnerability in the Northern High Plains. 100th American Meteorological Society Annual Meeting, Boston, MA. January 15, 2020.
  • Wilson, A. M., R. Cifelli, A. Dufour, T. W. Parzybok, M. Dettinger, J. A Vano, F. Munoz-Arriola, K. A. Miller. Toward greater resilient water infrastructure to future hydrometeorological extremes: Lessons from Orville dam and Hhurricane Harvey. 100th American Meteorological Society Annual Meeting, Boston, MA. January 15, 2020.
  • Kausik Ghosh, Francisco Munoz-Arriola. Understanding Geopolitically Contentious River Basin between India and Bangladesh: The Role of Changing Climate and Water Infrastructures in the Himalayan River Tista. American Geophysical Union, Fall Conference, Washington, DC. December 13th 2019.
  • Khan, M., C. Wunderlin, P. Sarzaeim, W. Ou, and F. Munoz-Arriola. Decoupling the Hydro-climatological condition before and during the recent flooding event in the Missouri River Basin. 100th American Meteorological Society Annual Meeting, Boston, MA. January 13, 2020.
  • Kausik Ghosh, Francisco Munoz-Arriola. Hydroclimate and Anthropogenic Drivers of Streamflow Pulses in the Himalayan River Tista, India. American Geophysical Union, Fall Conference, Washington, DC. December 11th, 2019.
  • Cifelli, Robert, Anna Maria Wilson, Alexis Dufour, Tye W Parzybok, Michael D Dettinger, Julie A Vano, Francisco Munoz-Arriola, Kathleen Anne Miller. Toward Greater Resilient Water Infrastructure to Future Hydrometeorological and Climate Extremes: Lessons from Oroville Dam and Hurricane Harvey. American Geophysical Union, Fall Conference, Washington, DC. December 11th, 2019.
  • Munoz-Arriola, F., A. Amaranto, P. Sarzaeim, L. Alves-Oliveira. Mapping the reliability of semi-seasonal forecasts of groundwater-levels using machine learning across the Northern High Plains. American Geophysical Union, Fall Conference, Washington, DC. December 11th, 2019.
  • Jarquin, D., F. Munoz-Arriola, P. Sarzaeim, and A. Amaranto. Geospatial assessment of phenotype predictive analytics using machine learning techniques and genome information. 2019 National Association Plant Breeders Conference. Pine Mountain, GA. August 27th, 2019. 

For the full list of presentations, software, articles, books, and procedings see the CV.