Cyber-Innovated Watershed Research at the Shale Hills Critical Zone Observatory

Cyberinfrastructure is enabling ever more integrative and transformative science. Technological advances in cyberinfrastructure have allowed deeper understanding of watershed hydrology by improved integration of data, information, and models. The synthesis of all sources of hydrologic variables (historical, real time, future scenarios, observed, and modeled) requires advanced data acquisition, data storage, data management, data integration, data mining, and data visualization. In this context, cyber-innovated hydrologic research was implemented to carry out watershed-based historical climate simulations at the Shale Hills Critical Zone Observatory. The simulations were based on the assimilation of data from a hydrologic monitoring network into a multiphysics hydrologic model (the Penn State Integrated Hydrology Model). We documented workflows for the model application and applied the model to short-time hyporheic exchange flow study and long-term climate scenario analysis. The effort reported herein demonstrates that advances in cyberscience allows innovative research that improves our ability to access and share data; to allow collective development of science hypotheses; and to support building models via team participation. We simplified communications between model developers and community scientists, software professionals, students, and decision makers, which in the long term will improve the utilization of hydrologic models for science and societal applications.

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

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

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

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

[5]  David M. Hannah,et al.  Inter‐disciplinary perspectives on processes in the hyporheic zone , 2011 .

[6]  Yolanda Gil,et al.  A Task-Centered Framework for Computationally-Grounded Science Collaborations , 2015, 2015 IEEE 11th International Conference on e-Science.

[7]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

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

[9]  W. J. Shuttleworth,et al.  COSMOS: the COsmic-ray Soil Moisture Observing System , 2012 .

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

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

[12]  Keith N. Eshleman,et al.  A linear model of the effects of disturbance on dissolved nitrogen leakage from forested watersheds , 2000 .

[13]  C. Lee Giles,et al.  Watershed Reanalysis: Towards a National Strategy for Model-Data Integration , 2011, 2011 IEEE Seventh International Conference on e-Science Workshops.

[14]  Yolanda Gil,et al.  OntoSoft: Capturing Scientific Software Metadata , 2015, K-CAP.

[15]  Yolanda Gil,et al.  Supporting Open Collaboration in Science Through Explicit and Linked Semantic Description of Processes , 2015, ESWC.

[16]  J. Kirchner Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology , 2006 .

[17]  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..

[18]  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 .

[19]  Mary C. Whitton,et al.  Water Science Software Institute: An open source engagement process , 2013, 2013 5th International Workshop on Software Engineering for Computational Science and Engineering (SE-CSE).

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

[21]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[22]  Geoffrey C. Fox,et al.  What is cyberinfrastructure , 2010, SIGUCCS '10.

[23]  Henry Lin Temporal Stability of Soil Moisture Spatial Pattern and Subsurface Preferential Flow Pathways in the Shale Hills Catchment , 2006 .