James Knighton

Ecohydrologic Considerations for Modeling of Stable Water Isotopes in a Small Intermittent Watershed

Naturally occurring stable water isotope tracers provide useful information for hydrologic model development and calibration. Existing models include varied approaches concerning unsaturated zone percolation mixing (preferential versus matrix flow) and evapotranspiration (ET) partitioning. We assess the impact of unsaturated zone simplifying assumptions when simulating the Shale Hills Watershed (SHW), a small (7.9 ha), temperate, forested watershed near Petersburg, Pennsylvania, USA with a relatively simple model. We found that different model structures/assumptions and parameterizations of unsaturated zone percolation had substantial impacts on the agreement between simulated and observed unsaturated-zone water isotopic signatures. We show that unsaturated zone percolation mixing primarily affects the unsaturated zone δ18O and δ2H during winter and spring and that percolation was best represented as a combination of both preferential and matrix flow. We evaluate the importance and implications related to the partitioning of ET into evaporation (E) and transpiration (T) and demonstrated that incorporation of a plant growth model for ET partitioning substantially improved reproduction of observed hydrologic isotopic patterns of the unsaturated zone during the spring season. We show that unsaturated zone percolation mixing and ET partitioning approaches do not substantially influence stream δ18O and δ2H and conclude that observed streamflow isotopic data is not always a strong predictor of model performance with respect to intra-watershed processes.

A proposed probabilistic seismic tsunami hazard analysis methodology

Abstract We expand on existing probabilistic tsunami hazard analysis (PTHA) approaches by presenting a methodology wherein the epistemic uncertainties associated with tsunami timing, generation, and propagation are separated from aleatory uncertainties. The contributions of epistemic and aleatory uncertainties to the overall uncertainty in tsunami hazard are evaluated through a two-level Monte Carlo analysis. The total epistemic uncertainty contribution defines the variance of the tsunami inundation hazard distribution. Through Bayesian inference, the available catalog of tsunami runup data is used to refine the hazard estimate, where the prior distribution is weighted by epistemic uncertainties. The general PTHA framework is further modified to include a hazard function accounting for peak water elevation, velocity, duration of inundation, and warning time. We carry out a formal sensitivity analysis of the tsunami generation and propagation on the feasible model parameter space using the multi-objective generalized sensitivity analysis algorithm. Parameters showing a significant influence on tsunami hazard include the tidal elevation, moment magnitude (Mw), the Gutenberg–Richter (G–R) distribution β parameter, epicenter location, and the rake angle. Parameters showing minimal influence on tsunami hazard include the G–R maximum seismic moment magnitude (Mmax) parameter, strike angle, dip angle, seismic depth and Manning’s roughness. As a proof of concept, the methodology is applied to a case study of tsunami hazard posed to National Oceanic and Atmospheric Administration Station 9411406 (Oil Platform Harvest, CA, USA) by seismic sources along the Aleutian Islands, AK.

Hydrologic State Influence on Riverine Flood Discharge for a Small Temperate Watershed (Fall Creek, United States): Negative Feedbacks on the Effects of Climate Change

AbstractWatershed flooding is a function of meteorological and hydrologic catchment conditions. Climate change is anticipated to affect air temperature and precipitation patterns such as altered total precipitation, increased intensity, and shorter event durations in the northeastern United States. While significant work has been done to estimate future meteorological conditions, much is currently unknown about future changes to distributions of hydrologic state variables. High-resolution hydrologic simulations of Fall Creek (Tompkins County, New York), a small temperate watershed (324 km2) with seasonal snowmelt, are performed to evaluate future climate change impacts on flood hydrology. The effects of hydrologic state and environmental variables on river flood stage are isolated and the importance of groundwater elevation, unsaturated soil moisture, snowpack, and air temperature is demonstrated. It is shown that the temporal persistence of these hydrologic state variables allows for an influence on wate...

A Vulnerability‐Based, Bottom‐up Assessment of Future Riverine Flood Risk Using a Modified Peaks‐Over‐Threshold Approach and a Physically Based Hydrologic Model

There is a chronic disconnection among purely probabilistic flood frequency analysis of flood hazards, flood risks, and hydrological flood mechanisms, which hamper our ability to assess future flood impacts. We present a vulnerability-based approach to estimating riverine flood risk that accommodates a more direct linkage between decision-relevant metrics of risk and the dominant mechanisms that cause riverine flooding. We adapt the conventional peaks-over-threshold (POT) framework to be used with extreme precipitation from different climate processes and rainfall-runoff-based model output. We quantify the probability that at least one adverse hydrologic threshold, potentially defined by stakeholders, will be exceeded within the next N years. This approach allows us to consider flood risk as the summation of risk from separate atmospheric mechanisms, and supports a more direct mapping between hazards and societal outcomes. We perform this analysis within a bottom-up framework to consider the relevance and consequences of information, with varying levels of credibility, on changes to atmospheric patterns driving extreme precipitation events. We demonstrate our proposed approach using a case study for Fall Creek in Ithaca, NY, USA, where we estimate the risk of stakeholder-defined flood metrics from three dominant mechanisms: summer convection, tropical cyclones, and spring rain and snowmelt. Using downscaled climate projections, we determine how flood risk associated with a subset of mechanisms may change in the future, and the resultant shift to annual flood risk. The flood risk approach we propose can provide powerful new insights into future flood threats.