Enabling Collaborative Numerical Modeling in Earth Sciences using Knowledge Infrastructure

Abstract Knowledge infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use knowledge infrastructure to address common barriers to entry into numerical modeling in Earth sciences as demonstrated in three computational narratives: physical process modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) community-supported collaborative web platform; 3) distributed data storage; 4) software environment; 5) personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research using Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools. Landlab notebooks are available for interactive computing on HydroShare at https://doi.org/10.4211/hs.fdc3a06e6ad842abacfa5b896df73a76 and for further development on Github at https://zenodo.org/badge/latestdoi/187289993 .

[1]  David R. Maidment,et al.  Hydrology's efforts toward the cyberfrontier , 2006 .

[2]  Nicole M. Gasparini,et al.  The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds , 2017 .

[3]  Laurent Pfister,et al.  Debates—Hypothesis testing in hydrology: Theory and practice , 2017 .

[4]  A. Wiggins,et al.  Project-based learning: A review of the literature , 2016 .

[5]  G. R. Foster,et al.  WEPP: A new generation of erosion prediction technology , 1991 .

[6]  David S. G. Thomas,et al.  World atlas of desertification. , 1994 .

[7]  Alex Hardisty,et al.  Curating Scientific Information in Knowledge Infrastructures , 2018, Data Sci. J..

[8]  Lan Zhao,et al.  SWATShare - A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models , 2016, Environ. Model. Softw..

[9]  Christopher Hutton,et al.  Most computational hydrology is not reproducible, so is it really science? , 2016, Water Resources Research.

[10]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[11]  Peter T. Darch,et al.  Knowledge infrastructures in science: data, diversity, and digital libraries , 2015, International Journal on Digital Libraries.

[12]  R. Sidle A theoretical model of the effects of timber harvesting on slope stability , 1992 .

[13]  Carol J. Ormand,et al.  Advantages of Computer Simulation in Enhancing Students' Learning About Landform Evolution: A Case Study Using the Grand Canyon , 2016 .

[14]  Alva L. Couch,et al.  HydroShare: Advancing Collaboration through Hydrologic Data and Model Sharing , 2015 .

[15]  Shaowen Wang CyberGIS and spatial data science , 2016 .

[16]  P. N. Edwards,et al.  Knowledge Infrastructures: Intellectual Frameworks and Research Challenges , 2013 .

[17]  Shaowen Wang A CyberGIS Framework for the Synthesis of Cyberinfrastructure, GIS, and Spatial Analysis , 2010 .

[18]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[19]  Matthew Baker,et al.  Extraction of hydrological proximity measures from DEMs using parallel processing , 2011, Environ. Model. Softw..

[20]  Jeffery S. Horsburgh,et al.  Design of a metadata framework for environmental models with an example hydrologic application in HydroShare , 2017, Environ. Model. Softw..

[21]  et al.,et al.  Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.

[22]  P. Schloss,et al.  Dynamics and associations of microbial community types across the human body , 2014, Nature.

[23]  Enrique R. Vivoni,et al.  Modeling the ecohydrological role of aspect‐controlled radiation on tree‐grass‐shrub coexistence in a semiarid climate , 2013 .

[24]  Martyn P. Clark,et al.  Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .

[25]  Peter Freeman,et al.  Cyberinfrastructure for Science and Engineering: Promises and Challenges , 2005, Proceedings of the IEEE.

[26]  L. Dilling,et al.  Drivers of adaptation: Responses to weather- and climate-related hazards in 60 local governments in the Intermountain Western U.S. , 2017 .

[27]  John E. Gilley,et al.  Water Erosion Prediction Project (WEPP): Development History, Model Capabilities, and Future Enhancements , 2007 .

[28]  Scott D. Kahn,et al.  Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.

[29]  Herbert Van de Sompel,et al.  Object Re-Use & Exchange: A Resource-Centric Approach , 2008, ArXiv.

[30]  Dmitri Kavetski,et al.  Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .

[31]  Juliana Freire,et al.  Reproducibility of Data-Oriented Experiments in e-Science (Dagstuhl Seminar 16041) , 2016, Dagstuhl Reports.

[32]  Jirí Kadlec,et al.  WaterML R package for managing ecological experiment data on a CUAHSI HydroServer , 2015, Ecol. Informatics.

[33]  Michael S. Eldred,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual. , 2010 .

[34]  Jeffery S. Horsburgh,et al.  Measuring water use, conservation, and differences by gender using an inexpensive, high frequency metering system , 2017, Environ. Model. Softw..

[35]  Jeffery S. Horsburgh,et al.  HydroShare: Sharing Diverse Environmental Data Types and Models as Social Objects with Application to the Hydrology Domain , 2016 .

[36]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .

[37]  Enrique R. Vivoni,et al.  Ecohydrologic role of solar radiation on landscape evolution , 2015 .

[38]  David R. Montgomery,et al.  Influence of precipitation phase on the form of mountain ranges , 2008 .

[39]  Jeffrey M. Perkel,et al.  Pioneering ‘live-code’ article allows scientists to play with each other’s results , 2019, Nature.

[40]  N. Gasparini,et al.  Measuring the imprint of orographic rainfall gradients on the morphology of steady‐state numerical fluvial landscapes , 2015 .

[41]  Ryan K. Orosco,et al.  Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss , 2014, Nature Genetics.

[42]  G. Tucker,et al.  Modelling landscape evolution , 2010 .

[43]  Greg Brown,et al.  Local government response to the impacts of climate change: An evaluation of local climate adaptation plans , 2012 .

[44]  Wei Luo,et al.  A web-based interactive landform simulation model (WILSIM) , 2004, Comput. Geosci..

[45]  Shaowen Wang,et al.  A CyberGIS-Jupyter Framework for Geospatial Analytics at Scale , 2017, PEARC.

[46]  Paul D. Bates,et al.  Improving the stability of a simple formulation of the shallow water equations for 2‐D flood modeling , 2012 .

[47]  Christine J. Kirchhoff,et al.  Narrowing the climate information usability gap , 2012 .

[48]  William A. Walters,et al.  Collaborative cloud-enabled tools allow rapid, reproducible biological insights , 2012, The ISME Journal.

[49]  Jodi Forlizzi,et al.  The building blocks of experience: an early framework for interaction designers , 2000, DIS '00.

[50]  Heleen L. P. Mees Local governments in the driving seat? A comparative analysis of public and private responsibilities for adaptation to climate change in European and North-American cities , 2017 .

[51]  Sean D. Mooney,et al.  Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and intercomparison , 2019, Environ. Model. Softw..

[52]  S. Jones,et al.  Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions , 2017 .

[53]  Maggi Savin-Baden,et al.  The problem of projects: understanding the theoretical underpinnings of project-led PBL , 2013 .

[54]  James H Stagge,et al.  Assessing data availability and research reproducibility in hydrology and water resources , 2019, Scientific Data.

[55]  Brian A. Nosek,et al.  Promoting an open research culture , 2015, Science.

[56]  Nicole M. Gasparini,et al.  Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics , 2016 .

[57]  Kevin M. Brooks Do story agents use rocking chairs? The theory and implementation of one model for computational narrative , 1997, MULTIMEDIA '96.

[58]  William J. Elliot,et al.  WEPP-Predicting water erosion using a process-based model , 1997 .

[59]  D. Tarboton,et al.  Modeling of the interactions between forest vegetation, disturbances, and sediment yields , 2004 .

[60]  Jeffery S. Horsburgh,et al.  Observations Data Model 2: A community information model for spatially discrete Earth observations , 2016, Environ. Model. Softw..

[61]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[62]  J. Horsburgh,et al.  High-frequency measurements reveal spatial and temporal patterns of dissolved organic matter in an urban water conveyance , 2017, Environmental Monitoring and Assessment.

[63]  Alva L. Couch,et al.  A Resource Centric Approach for Advancing Collaboration Through Hydrologic Data and Model Sharing , 2014 .

[64]  Alva L. Couch,et al.  Advancing distributed data management for the HydroShare hydrologic information system , 2018, Environ. Model. Softw..

[65]  Nicole M. Gasparini,et al.  A hydroclimatological approach to predicting regional landslide probability using Landlab , 2017 .

[66]  Helen Shen,et al.  Interactive notebooks: Sharing the code , 2014, Nature.

[67]  Murugesu Sivapalan,et al.  Progress in socio‐hydrology: a meta‐analysis of challenges and opportunities , 2017 .

[68]  Tanu Malik,et al.  Utilizing Provenance in Reusable Research Objects , 2018, Informatics.

[69]  Johanna Nalau,et al.  Is adaptation a local responsibility , 2015 .