HIHRG - Research

Recent publications

Journal articles

  • Ojeda Bustamante, W., Palerm Viqueira, J., and Muñoz-Arriola, F. (2024). Las paradojas de la eficiencia de riego asociadas a la tecnificación de zonas de riego: cuentos y cuentas. Revista del Colegio de San Luis. DOI TBD.

  • Carrillo, C.M.; Muñoz-Arriola, F. (2024). Assessing Multi-Scale Atmospheric Circulation Patterns for Improvements in Sub-Seasonal Precipitation Predictability in the Northern Great Plains. Atmosphere, 15, 858. https://doi.org/10.3390/ atmos15070858.

  • Sarzaeim, P.; Muñoz-Arriola, F. (2024). A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada. Agronomy,14,733. https:// doi.org/10.3390/agronomy14040733. 
  • Sarzaeim, P.; Muñoz-Arriola, F. A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada (2024). Preprints 2024,2024030999.https://doi.org/10.20944/preprints202403.0999.v1. 
  • Stuart, L., Hobbins, M., Niebuhr, E., Ruane, A.C., Pulwarty, R. Hoell, A., Thiaw, W., Rosenzweig, C., Muñoz-Arriola, F. Jahn, M. Farrar, M. (2024) Enhancing Global Food Security: Opportunities for the American Meteorological Society. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-22-0106.1
  • Carrillo, C.M.2; Muñoz-Arriola, F.; Chen, L. (2023). Multi-scale Sources of Precipitation Predictability in the Northern Great Plains. Preprints 2023120362. https://doi.org/10.20944/preprints202312.0362.v.1
  • Quiñones R, Samal A., Das Choudhury S. and Muñoz-Arriola, F. (2023) OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features. Front. Plant Sci. 14:1211409. doi: 10.3389/fpls.2023.1211409.
  • Sarzaeim, P., Muñoz-Arriola, F., Jarquin, D., Aslam, H., & De Leon Gatti, N. (2023). CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada. Earth System Science Data, 15(9), 3963-3990.https://doi.org/10.5194/essd-15-3963-2023.
  • Sarzaeim, P., F. Munoz-Arriola, D. Jarquin, H. Aslam, and N. De Leon Gatti. CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the US and Canada. Earth System Science Data Discussions (2023): 1-35. https://doi.org/10.5194/essd-2023-11.
  • Ghosh3, K., and F. Munoz-Arriola (2023). Hysteresis and streamflow-sediment relations across the continuum of natural-to-post dam construction in a highly regulated transboundary Himalayan River. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2023.129885.

  • Rehana3, S., Y. Pranathi, G. Basha, and F. Munoz-Arriola (2022). Precipitation and Temperature Extremes and Association with Large-scale Climate Indices: An Observational Evidence over India. Journal of Earth System Science. https://doi.org/10.1007/s12040-022-01911-3.
  • Jaimes-Correa1, J.C., F. Munoz-Arriola, and S. Bartelt-Hunt (2022). Modeling water quantity and quality nonlinearities for watershed adaptability to hydroclimate extremes in agricultural landscapes. Hydrology.  https://doi.org/10.3390/hydrology9050080.
  • Sarzaeim1, P., F. Muñoz-Arriola, and D. Jarquin (2022). Climate and genetic data enhancement using deep learning analytics to improve maize yield predictabilitys. Journal of Experimental Botany.DOI: 10.1093/jxb/erac146.

  • Muñoz-Arriola, F. and V. Macias-Zamora (2022) Geospatial Synthesis of Biogeochemical Attributions of Porphyrins to Oil Pollution in Marine Sediments of the Gulf of México. Geosciences. https://doi.org/10.3390/ geosciences12020077.

  • Singh3, V. and F. Munoz-Arriola (2021). Improvements in fractional snowpack and snowmelt simulations at sub-catchment scale climate change assessments in the Western Himalayas. Hydrology. https://doi.org/10.3390/hydrology8040179.

  • Quiñones, R.1, F. Muñoz-Arriola, S. Das Chouhdury, and A. Samal (2021). Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLUS-One. https://doi.org/10.1371/journal.pone.0257001.

  • Quiñones, R.1, F. Muñoz-Arriola, S. Das Chouhdury, and A. Samal (2021). Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System. https://doi. org/10.5281/zenodo.5117176.

  • 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.
  • Ashish Kumar1, RAAJ Ramsankaran3, 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, D. Han (2020). Multi-Satellite Precipitation Products for Meteorological Drought Assessment and Forecasting in Bundelkhand region of Central India. Geocarto.  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..

  • 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.
  • 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.

  • Preisler, H. K., A. L. Westerling, K. M. Gebert, F. Munoz-Arriola, and T. P. Holmes (2011). Spatially Explicit Forecasts of Large Fire Probability and Suppression Costs for California Federal and State Lands. International Journal of Wildland Fire, 20(4), 508-517.

  • Muñoz Arriola, F. J. H. Salgado Rabadán, H. M. Rocchiccioli, S. Shukla, A. Güitrón De los Reyes, y R. Lobato Sánchez (2011). Surface Hydrology in the Grijalva River Basin: Calibration of the Variable Infiltration Capacity Model. Aqua-LAC, 3(1), 68-79.

  • Shefield, J.,  E. Wood, and F. Munoz-Arriola (2010). Long-term regional estimates of evapotranspiration for Mexico based on downscaled ISCCP data. Journal of Hydrometeorology, 11(2), 253-275.

  • Munoz-Arriola, F. D.P. Lettenmaier, C. Zhu, and R. Avissar (2009). Water resources sensitivity of the Rio Yaqui Basin, México to agriculture extensification under multi-scale climate conditions. Water Resources Research, 45, W00A20, doi:10.1029/2007WR006783.

  • Munoz-Arriola, F., Shraddhanand Shukla, Theodore J. Bohn, Chunmei Zhu, Ben Livneh, Dennis P. Lettenmaier, René Lobato Sánchez, and Ana Wagner Gomez (2009). Forecasting Surface Hydrology in North America. Border Climate Summary, July 2009: 1-5.

  • Munoz-Arriola, F., D. P. Lettenmaier, C. Zhu, A. W. Wood, R. Lobato Sánchez, and A. Wagner Gomes (2008). Extended West-wide Seasonal Hydrological System: Seasonal Hydrological Prediction in the NAMS region. CLIVAR Exchanges, 43: 24-25.

  • Muñoz Arriola, F., J. D. Carriquiry-Beltran, E. Nieto-Garcia, and M. Hernandez-Ayon (1999). Colorado River Delta, In: Mexican and Central American Coastal Lagoon Systems: Carbon, Nitrogen and Phosphorus Fluxes. S.V. Smith. LOICZ Reports and Studies No. 13, pp 59-69

BOOKS

  • Lamine, S., P.K. Srivastava, A. Kayad, P., F. Munoz-Arriola, P.C. Pandey (2023). REMOTE SENSING IN PRECISION AGRICULTURE: TRANSFORMING SCIENTIFIC ADVANCEMENT INTO INNOVATION. Elsevier books/Academic Press.  ISBN 978-0-323-91068-2.
  • Singh, Vijay P., Shalini Yadav, Krishna K. Yadav, Gerald A. Corzo Perez, Francisco Munoz-Arriola, and Ram N. Yadava (2022). APPLICATION OF REMOTE SENSING AND GIS IN NATURAL RESOURCES AND BUILT INFRASTRUCTURE MANAGEMENT. ISBN-13: 978-3-031-14095-2.

  • Ramanathan, A.L.R., S. Chidambaram, M.P. Jonathan, M.V. Prasana, P. Kumar, and F. Munoz-Arriola (2021). ENVIRONMENTAL RESILIENCE AND TRANSFORMATION IN TIMES OF COVID-19: CLIMATE CHANGE EFFECTS ON ENVIRONMENTAL FUNCTIONALITY. Elsevier.ISBN# 978-0323855129. 438pp.

  • Kumar, M., F. Munoz-Arriola, H. Furumai, and T Chaminda (2020) RESILIENCE, RESPONSE, AND RISK IN WATER SYSTEMS: SHIFTS IN NATURAL FORCINGS AND MANAGEMENT PARADIGMS. Springer Transactions in Civil and Environmental Engineering. ISBN#978-981-15-4667-9: 395pp.

CHAPTERS

  • Janin1, J., S. Choudhary, F. Munoz-Arriola, and D. Khare (2023). Remote Sensing and Machine Learning Applications for the Assessment of Urban Water Stress: A Review. In: Emerging Technologies for Water Supply, Conservation and Management.
  • Janin, J., F. Munoz-Arriola, and D. Khare (2021).  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.DOI: 10.1016/C2020-0-02703-9.

  • Shaik 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. 

  • 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.

  • 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.

  • Muñoz Orozco, A. and F Muñoz Arriola (2010). Water, Climate, and Agro-ecological Systems: Past, Present, and Challenges. In: Lectures in Etnobotany. Ed. J.A. Cuevas Sanchez. (In Spanish). 

PROCEEDINGS

  • Rico1, D. A., F. Muñoz-Arriola, J. Bradley, and C. Detweiler (Accepted). Analytics for Real-Time Inertial Localization of the Tethered Aircraft Unmanned System. 18th International Symposium on Experimental Robotics (ISER), 2023, 11 pp.
  • Ojeda Bustamante, W., Palerm Viqueira, J., and Muñoz-Arriola, F. (2023). El ahorro del agua a través de la tecnificación de grandes zonas de riego: cuentas y cuentos. COMEII-23003. VIII Congreso Nacional y I Congreso Internacional de Riego, Drenaje y Biosistemas, October 6, 2023.
  • Heeren, D.M., Hayde, L.G., Eisenhauer, D.E., McCornick, P.G., Mohammed, A.T., Mittelstet, A.R., Boldt, A.L., Qiao, X., Mabie, D.M. and Munoz-Arriola, F. (2023). A Graduate-Level Field Course in Irrigation and Agricultural Water Management for an Immersive Learning Experience. In 2023 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. doi:10.13031/aim.202301165.

  • Romanelli, T., F. Muñoz-Arriola, and A. F. Colaço (2022). Conceptual framework to integrate economic drivers of decision-making for technology adoption in agriculture. Engineering Proceedings, 43 (9): pp 1-5. https://doi.org/10.3390/ engproc2021009043. (peer reviewed)

  • Rico1, D. A., F. Muñoz-Arriola, and C. Detweiler (2021). Trajectory Selection for Power-over-Tether Atmospheric Sensing UAS. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 2321-2328, doi: 10.1109/IROS51168.2021.9636364. (peer reviewed)
  • Sarzaeim1, P., W. Ou1, L. Alves de Oliveira2, and F. Munoz-Arriola (2021). Spatiotemporal diagnostics and prognostics of major crops’ vulnerabilities to flooding in the Northern High Plains. ASCE’s Proceedings of GEO-Extreme 2021.(peer reviewed)

  • Wilson, A., R. Cifelli, F. Munoz-Arriola, T. Parzybok, J. Giobannetone, J. Vano and M. Dettinger (2021). Toward Building Infrastructure Resiliency to Future Hydroclimate Extremes: A Case Study Investigation. ASCE’s Proceedings of GEO-Extreme 2021.(peer reviewed)

  • Rico, D.A., Carrick Detweiler, 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, P., 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 Williams, Parisa Sarzaeim, 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. ASABE Annual International Meeting, Paper No. 1900852, Boston, Mass. 19 pages. https://doi.org/10.13031/aim.201900852.

  • 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.(peer reviewed)

  • 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

  • Sarzaeim1, P., & Munoz-Arriola, F. (2023). GSA-GxE: A Framework of Global Sensitivity Analysis of Maize Coupled with Genetics by Environments (GxE) Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8393662
  • Aslam1, H, Sarzaeim1, P., and F. Muñoz-Arriola (2023). CLImate for Maize OMICS: CLIM4OMICS Analytics and Database. DOI 10.5281/zenodo.8002909.
  • Aslam1, H, Sarzaeim1, P., and F. Muñoz-Arriola (2023). CLImate-for-Maize-OMICS_CLIM4OMICS-Analytics-and-Database Code. DOI 10.5281/zenodo.8161662.
  • Jaimes-Correa1, J.C., F. Munoz-Arriola, and S. Bartelt-Hunt (2022). Water quantity and quality datasets for the simulation of nonlinearities for watershed adaptability to hydroclimate extremes in agricultural landscapes. DOI: 10.3390/hydrology9050080.
  • Sarzaeim1, P., F. Muñoz-Arriola, and D. Jarquin (2022). Large-scale and Multi-dimensional Climate, Genetics, and Phenotypes Database for Maize Yield Predictability in the U.S. and Canada. https://doi.org/10.5281/zenodo.6299090.
  • Munoz-Arriola, F. and V. Macias-Zamora (2022). Database for the Geospatial Synthesis of Biogeochemical Attributions of Porphyrins to Oil Pollution in Marine Sediments of the Gulf of México. https://doi.org/10.5281/zenodo.5979556.
  • Carrillo, C.M. and F. Munoz-Arriola (2021). Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains. DOI 10.5072/zenodo.983162.
  • Quiñones, R.1, F. Muñoz-Arriola, S. Das Chouhdury, and A. Samal (2021). Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System. https://doi. org/10.5281/zenodo.5117176.
  • Hohbein, H., A. Zhang, Z. Trautman, D. Brecic, and J. Carter. P. Sarzaeim, D. Jarquin, 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.
  • Rico1, D. A., J. Korus, and F. Munoz-Arriola (2017). Mining Alphanumerical Stratigraphic Data for Aquifer Diagnosis and Ground water-level Forecast.
  • Rico4, D. and F. Munoz-Arriola (2016). Seasonal Hydrological Forecast System: A Prototype. Funded by the Daugherty Water for Food Institute and UNL.
  • Munoz-Arriola, F. and G. Lopez-Morteo3. (1) Two data-collectors of data via web services programed in Python and Java; (2) six APIs that standardize/translate/deliver data in multiple formats (CSV, JSON, netCDF, postgreSQL, SPSS, HDF and text); (3) three apps (2 for smart phone and webpage); (4) analytics to address crop, livestock, and community needs.