Featured Collection Introduction: National Water Model IV

In 2015, the National Oceanic and Atmospheric Administration (NOAA) established Office of Water Prediction (OWP) in Weather Service (NWS). The mission OWP is to “collaboratively research, develop deliver timely consistent, state-of-the-science national hydrologic analyses, forecast information, data, guidance, decision-support services inform essential emergency management water resources decisions across all time scales” (NOAA 2022). team works create a consistent unified NWS program with goal supporting “water-ready nation” capable addressing nation’s challenges. To support OWP, also Center (NWC), which first opened its doors May 2015. NWC, located on campus University Alabama Tuscaloosa, was created new generation information nation that strengthening capabilities for floods droughts improving preparedness water-related disasters. partnership Consortium Universities Advancement Hydrologic Science, Inc. (CUAHSI) Summer Innovators Program Institute (SI) engage academic community research advance NWC (CUAHSI 2018, Model (NWM) current basis continental-scale predictions by OWP. NWM became operational August 16, 2016 delivers 18-h streamflow forecasts about 2.7 million stream reaches continental United States (U.S.), as well 10- 30-day ensemble forecasts. based Research’s WRF-Hydro framework (NCAR SI seven-week residential at during graduate students, postdoctoral scientists, faculty advisors, staff, other professionals collaboratively conduct projects related prediction. fifth annual held June 10–July 26, 2019 involved 18 students from 16 unique universities. Cumulatively, 2015 2021, 160 82 universities participated SI. addition 66 individuals have SIs instructors and/or mentors, representing universities; federal, state, local agencies; private sector; nonprofit organizations. 2020 canceled due pandemic 2021 scaled-down virtual event. participants self-form teams two five participants, each conducts project one predetermined themes seek some aspect Themes were (1) Coupled Inland-Coastal Hydraulics total prediction, including compound surge upland flooding; (2) Scaling Processes understand how models operating large grid scales might perform local-scale issues such flash (3) Hydroinformatics addressed workflows. All student are summarized CUAHSI Technical Report (Flint et al. 2019). Projects utilized version 1.2 5.0. This featured collection six papers, authored both documents outcomes, findings performed Addressing challenges includes predicting floods, droughts, increasingly, storm surge. Although much recent focus has been flood inundation forecasting prediction required respond surge, rising sea levels, flooding. Three papers (Chegini 2022; Muñoz 2022) this address challenge coastal zone, requires tool than NWM, did not zone level Performance 2D HEC-RAS (Brunner 2016) Delft3d-FM (Deltares 2019a, 2019b) (TWL) Delaware Bay evaluated (2022) under Hurricane Sandy, occurred 2012 (Blake 2013) conditions. Their results suggest inclusion atmospheric forcing (wind barometric pressure) important TWL Delft3D-FM significantly improve predictions, but less so HEC-RAS. Furthermore, model simulations can be faster factor 6 10, outperformed dominated freshwater inflow forcing. Despite these advantages, noncommercial software package easier implement simpler alternative modeling extreme events when dominant. companion paper, Yin performance hydrodynamic ADCIRC (Luettich 1993) Isabel particular application, true coupling achieved using predict inflows, simulate hydrodynamics nearshore provide boundary conditions ocean. framework, however, include component simulating changes associated waves or effects direct precipitation domain. Nevertheless, coupled reproducing observed variations, demonstrating feasibility While specific event models, Chegini sought general process studies understanding landward extent influences predictions. A investigating contributions different processes parameters transition zones proposed. accounts geometric shape system, physical properties, proposed property they term Tidal Vanishing Point (TVP) identifying location transitions purely tidal entirely discharge-driven process. Insight into TVP help developers make calibration achieving computational efficiency estimations only relevant inputs features. study does comprehensive evaluation factors influencing TVP, generic extensible. Two Barriers adoption use computationally intensive distributed benefit user friendly tools setting up running adjust explore their parameter space. Raney demonstrated Dockerized Job Scheduler (DJS) an open-source Python library utilizing modeling-as-a-service concepts hardware level. DJS simplifies lowers technical barrier usage incorporating Docker technology easy-to-use command line interface, removing necessity dependency compilation. varied sets orchestrate concurrent simulations. well-equipped enabling teaching applications, parametric sensitivity tasks, stochastic ensembles. another reproducible workflows, Finkenbiner Semmendinger developed workflow visualize uncertainties. easily adaptable transferrable designed easy integration workflow. study, adapted several domains three models: SWMM (Rossman 2015), HBV-light (Bergström 1992), TOPMODEL (Beven 1997). applied sufficiently it could output disciplines. An executable Jupyter Notebook R scripts included GitHub repository case presented paper. skill limited architecture utilizes uniform structure spatial scale entire domain (Johnson 2019), even though vary widely landscapes. next-generation attempting build enables heterogeneous, regionalized physics extent, integrating conceptual method. purpose work Kim evaluate replacing runoff algorithm, specifically subsurface component, simpler, algorithm (TOPMODEL; Beven Moreover, approaches evaluated. Results suggested representation calculation configuration (TOPMODEL) better characterized watershed. showed affected behavior sensitivity. published JAWR conducted collection, Nelson (2017) Flood Interoperability Experiment SI; Cohen (2018) provided introduction second describing Bales (2019) introduced third Flowers (2021) fourth collection. Research represented past collections helping science U.S. supported through grant Inc., NSF EAR-1849456 Administration, Service, Prediction.