Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

Sea‐level rise and increases in the frequency and intensity of ocean surges caused by climate change are likely to exacerbate adverse effects on low‐lying coastal areas. The landward flow of water during ocean surges introduces salt to surficial coastal aquifers and threatens groundwater resources. Coastal topographic features (e.g., ponds, dunes, barrier islands, and channels) likely have a strong impact on overwash and salinization processes, but are generally highly simplified in modeling studies. To understand topographic impacts on groundwater salinization, we modeled a theoretical overwash event and variable‐density groundwater flow and salt transport in 3‐D using the fully coupled surface and subsurface numerical simulator, HydroGeoSphere. The model simulates the coastal aquifer as an integrated system considering overland flow, coupled surface and subsurface exchange, variably saturated flow, and variable‐density groundwater flow. To represent various coastal landscape types, we simulated both synthetic fields and real‐world coastal topography from Delaware, USA. The groundwater salinization assessment suggested that the topographic connectivity promoting overland flow controls the volume of aquifer that is salinized. In contrast, the amount of water that can be stored in surface depressions determines the amount of seawater that infiltrates the subsurface and the time for seawater to flush from the aquifer. Our study suggests that topography has a significant impact on groundwater salinization due to ocean surge overwash, with important implications for coastal land management and groundwater vulnerability assessment.

A fully coupled land surface hydrologic model, Flux-PIHM, is developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Because PIHM is capable of simulating lateral water flow and deep groundwater at spatial resolutions sufficient to resolve upland stream networks, Flux-PIHM is able to represent heterogeneities due to topography and soils at high resolution, including spatial structure in the link between groundwater and the surface energy balance (SEB). Flux-PIHM has been implemented at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Multistate observations of discharge, water table depth, soil moisture, soil temperature, and sensible and latent heat fluxes in June and July 2009 are used to manually calibrate Flux-PIHM at hourly temporal resolution. Model predictions from 1 March to 1 December 2009 are evaluated. Both hydrologic predictions and SEB predictions show good agreement with observations. Comparisons of model predictions between Flux-PIHM and the original PIHM show that the inclusion of the complex SEB simulation only brings slight improvement in hourly model discharge predictions. Flux-PIHM adds the ability of simulating SEB to PIHM and does improve the prediction of hourly evapotranspiration, the prediction of total runoff (discharge), and the predictions of some peak discharge events, especially after extended dry periods. Model results reveal that annual average sensible and latent heat fluxes are strongly correlated with water table depth, and the correlation is especially strong for the model grids near the stream.

[1] This paper presents multiple parameter estimation using multivariate observations via the ensemble Kalman filter (EnKF) for a physically based land surface hydrologic model. A data assimilation system is developed for a coupled physically based land surface hydrologic model (Flux-PIHM) by incorporating EnKF for model parameter and state estimation. Synthetic data experiments are performed at a first-order watershed, the Shale Hills watershed (0.08 km²). Six model parameters are estimated. Observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. The results show that, given a limited number of site-specific observations, the EnKF can be used to estimate Flux-PIHM model parameters. All the estimated parameter values are very close to their true values, with the true values inside the estimated uncertainty range (1 standard deviation spread). The estimated parameter values are not affected by the initial guesses. It is found that discharge, soil moisture, and land surface temperature (or sensible and latent heat fluxes) are the most critical observations for the estimation of those six model parameters. The assimilation of multivariate observations applies strong constraints to parameter estimation, and provides unique parameter solutions. Model results reveal strong interaction between the van Genuchten parameters α and β, and between land surface and subsurface parameters. The EnKF data assimilation system provides a new approach for physically based hydrologic model calibration using multivariate observations. It can be used to provide guidance for observational system designs, and is promising for real-time probabilistic flood and drought forecasting.