Open science in practice: Learning integrated modeling of coupled surface‐subsurface flow processes from scratch

Integrated modeling of coupled surface-subsurface flow and ensuing role in diverse Earth system processes is of current research interest to characterize nonlinear rainfall-runoff response and also to understand land surface energy balances, biogeochemical processes, geomorphological dynamics, etc. A growing number of complex models have been developed for water-related research, and many of these are made available to the Earth science community. However, relatively few resources have been made accessible to the potentially large group of Earth science and engineering users. New users have to invest an extraordinary effort to study the models. To provide a stimulating experience focusing on the learning curve of integrated modeling of coupled surface-subsurface flow, we describe use cases of an open source model, the Penn State Integrated Hydrologic Model, PIHM. New users were guided through data processing and model application by reproducing a numerical benchmark problem and a real-world watershed simulation. Specifically, we document the PIHM application and its computational workflow to enable intuitive understanding of coupled surface-subsurface flow processes. In addition, we describe the user experience as important evidence of the significance of reusability. The interaction shows that documentation of data, software, and computational workflow in research papers is a promising method to foster open scientific collaboration and reuse. This study demonstrates how open science practice in research papers would promote the utility of open source software. Addressing such open science practice in publications would promote the utility of journal papers. Further, popularization of such practice will require coordination among research communities, funding agencies, and journals.

[1]  James P. McNamara,et al.  Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics , 2015 .

[2]  Yu Zhang,et al.  Fully-coupled hydrologic processes for modeling landscape evolution , 2016, Environ. Model. Softw..

[3]  Hubert H. G. Savenije,et al.  Joint editorial: Fostering innovation and improving impact assessment for journal publications in hydrology , 2016, Hydrology and Earth System Sciences.

[4]  D. Lawrence,et al.  Improving the representation of hydrologic processes in Earth System Models , 2015 .

[5]  Christopher J. Duffy,et al.  Development of a Coupled Land Surface Hydrologic Model and Evaluation at a Critical Zone Observatory , 2013 .

[6]  Erika Check Hayden,et al.  Rule rewrite aims to clean up scientific software , 2015, Nature.

[7]  Gopal Bhatt,et al.  PIHM: PIHM version 2.2 , 2015 .

[8]  Luis Samaniego,et al.  The HyperHydro (H^2) experiment for comparing different large-scale models at various resolutions , 2015 .

[9]  Per-Olof Persson,et al.  A Simple Mesh Generator in MATLAB , 2004, SIAM Rev..

[10]  Christopher J. Duffy,et al.  Modelling long-term water yield effects of forest management in a Norway spruce forest , 2015 .

[11]  Christopher J. Duffy,et al.  Simulating high‐resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model , 2015 .

[12]  Jasper A. Vrugt,et al.  Reproducible Research in Vadose Zone Sciences , 2015 .

[13]  Yolanda Gil,et al.  Cyber-Innovated Watershed Research at the Shale Hills Critical Zone Observatory , 2016, IEEE Systems Journal.

[14]  Christopher J. Duffy,et al.  Essential Terrestrial Variable data workflows for distributed water resources modeling , 2013, Environ. Model. Softw..

[15]  C. Hunsaker,et al.  Hydrogeologic influence on changes in snowmelt runoff with climate warming: Numerical experiments on a mid-elevation catchment in the Sierra Nevada, USA , 2015 .

[16]  Mukesh Kumar,et al.  Toward a Hydrologic Modeling System , 2009 .

[17]  R. Maxwell,et al.  Interdependence of groundwater dynamics and land-energy feedbacks under climate change , 2008 .

[18]  Thomas Graf,et al.  Modelling the effects of tides and storm surges on coastal aquifers using a coupled surface-subsurface approach. , 2013, Journal of contaminant hydrology.

[19]  Danny Marks,et al.  Anomalous trend in soil evaporation in a semi-arid, snow-dominated watershed , 2012 .

[20]  Matthew S. Mayernik,et al.  Peer Review of Datasets: When, Why, and How , 2015 .

[21]  Charles R. Lane,et al.  Hydrologic connectivity between geographically isolated wetlands and surface water systems: A review of select modeling methods , 2014, Environ. Model. Softw..

[22]  P. Bryan Heidorn,et al.  Shedding Light on the Dark Data in the Long Tail of Science , 2008, Libr. Trends.

[23]  David G. Tarboton,et al.  An overview of current applications, challenges, and future trends in distributed process-based models in hydrology , 2016 .

[24]  Christopher J. Duffy,et al.  Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment , 2015 .

[25]  Christopher J. Duffy,et al.  Designing a Suite of Models to Explore Critical Zone Function , 2014, Procedia Earth and Planetary Science.

[26]  C. Duffy,et al.  A semidiscrete finite volume formulation for multiprocess watershed simulation , 2007 .

[27]  A. Hindmarsh,et al.  CVODE, a stiff/nonstiff ODE solver in C , 1996 .

[28]  Christian Hergarten,et al.  Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development , 2014, Front. Earth Sci..

[29]  Christopher J. Duffy,et al.  Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment , 2014 .

[30]  Christopher J. Duffy,et al.  A coupled surface-subsurface modeling framework to assess the impact of climate change on freshwater wetlands , 2015 .

[31]  Christopher S. Jazwa,et al.  Hydrological Modeling and Prehistoric Settlement on Santa Rosa Island, California, USA , 2016 .

[32]  Carol S. Woodward,et al.  Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers , 2020, ACM Trans. Math. Softw..

[33]  A. Royer,et al.  Integrating isolated and riparian wetland modules in the PHYSITEL/HYDROTEL modelling platform: model performance and diagnosis , 2015 .

[34]  Christopher J. Duffy,et al.  Understanding ancient Maya water resources and the implications for a more sustainable future , 2014 .

[35]  James Taylor,et al.  Next-generation sequencing data interpretation: enhancing reproducibility and accessibility , 2012, Nature Reviews Genetics.

[36]  E. Todini,et al.  A conservative finite elements approach to overland flow: the control volume finite element formulation , 1996 .

[37]  Jens Kattge,et al.  Carrots and sticks. , 2014, Newsweek.

[38]  Christopher J. Duffy,et al.  Parameterization for distributed watershed modeling using national data and evolutionary algorithm , 2012, Comput. Geosci..

[39]  Daniel F. Nadeau,et al.  Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data , 2014 .

[40]  Christopher J. Duffy,et al.  Automating data-model workflows at a level 12 HUC scale: Watershed modeling in a distributed computing environment , 2014, Environ. Model. Softw..

[41]  Scott D. Peckham The CSDMS Standard Names: Cross-Domain Naming Conventions for Describing Process Models, Data Sets and Their Associated Variables , 2014 .

[42]  Christopher J. Duffy,et al.  A tightly coupled GIS and distributed hydrologic modeling framework , 2014, Environ. Model. Softw..

[43]  J. Villeneuve,et al.  A Process‐Oriented, Multiple‐Objective Calibration Strategy Accounting for Model Structure , 2013 .

[44]  Olaf Kolditz,et al.  Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks , 2014 .

[45]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[46]  Steve Easterbrook,et al.  Open code for open science , 2014 .

[47]  Brian L. McGlynn,et al.  Variations in Streamflow Response to Large Hurricane-Season Storms in a Southeastern U.S. Watershed , 2015 .

[48]  Jon Harbor,et al.  What Is a Watershed? Implications of Student Conceptions for Environmental Science Education and the National Science Education Standards , 2007 .

[49]  Chaopeng Shen,et al.  Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface‐land surface process model and GRACE, MODIS products , 2014 .

[50]  Wenqing Wang,et al.  OpenGeoSys: an open-source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media , 2012, Environmental Earth Sciences.

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

[52]  Suzanne A. Pierce,et al.  Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance , 2016 .

[53]  Luc Moreau,et al.  The Foundations for Provenance on the Web , 2010, Found. Trends Web Sci..

[54]  Göran Lindström,et al.  Virtual laboratories: new opportunities for collaborative water science , 2014, Hydrology and Earth System Sciences.

[55]  Laura Paglione,et al.  ORCID: a system to uniquely identify researchers , 2012, Learn. Publ..

[56]  Christopher J. Duffy,et al.  Exploring the Role of Domain Partitioning on Efficiency of Parallel Distributed HydrologicModel Simulations , 2015 .

[57]  V. Stodden Trust your science? Open your data and code , 2011 .

[58]  Reed M. Maxwell,et al.  Numerical experiments to explain multiscale hydrological responses to mountain pine beetle tree mortality in a headwater watershed , 2016 .

[59]  Christopher J. Duffy,et al.  Visualization workflows for level-12 HUC scales: Towards an expert system for watershed analysis in a distributed computing environment , 2016, Environ. Model. Softw..

[60]  Ernesto Reuben,et al.  (Un)Available upon Request: Field Experiment on Researchers' Willingness to Share Supplementary Materials , 2012, Accountability in research.

[61]  Karen R. Dawkins,et al.  Eighth Grade Students' Understandings of Groundwater , 2004 .

[62]  Serge Massicotte,et al.  Determination of the drainage structure of a watershed using a digital elevation model and a digital river and lake network , 2001 .

[63]  René Therrien,et al.  Simulating coupled surface and subsurface water flow in a tile-drained agricultural catchment , 2015 .

[64]  Brooks Hanson,et al.  Liberating field science samples and data , 2016, Science.

[65]  Jeff Dozier,et al.  Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models , 2013 .

[66]  Wonsuck Kim,et al.  Data management, sharing, and reuse in experimental geomorphology: Challenges, strategies, and scientific opportunities , 2015 .

[67]  V. Acocella Grand challenges in Earth science: research toward a sustainable environment , 2015, Front. Earth Sci..

[68]  Richard Turcotte,et al.  Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent in southern Québec, Canada , 2007 .

[69]  Christopher S Lowry,et al.  CrowdHydrology: Crowdsourcing Hydrologic Data and Engaging Citizen Scientists , 2013, Ground water.

[70]  Jean-Pierre Villeneuve,et al.  DISTRIBUTED WATERSHED MODEL COMPATIBLE WITH REMOTE SENSING AND GIS DATA .I : D ESCRIPTION OF MODEL , 2001 .

[71]  Roger Barga,et al.  Automatic capture and efficient storage of e-Science experiment provenance , 2008 .

[72]  A synthetic hydrologic‐response dataset , 2011 .

[73]  Efi Foufoula-Georgiou,et al.  Toward a unified science of the Earth's surface: Opportunities for synthesis among hydrology, geomorphology, geochemistry, and ecology , 2006 .

[74]  Paul T. Groth,et al.  Ten Simple Rules for the Care and Feeding of Scientific Data , 2014, PLoS Comput. Biol..

[75]  Yolanda Gil,et al.  Discovery Informatics: AI Opportunities in Scientific Discovery , 2012, AAAI Fall Symposium: Discovery Informatics.