Danny Katzman

Sediment delivery after a wildfire

We use a record of sedimentation in a small reservoir within the Cerro Grande burn area, New Mexico, to document postfire delivery of ash, other fine-grained sediment carried in suspension within floods, and coarse-grained sediment transported as bedload over a five-year period. Ash content of sediment layers is estimated using fallout 137 Cs as a tracer, and ash concentrations are shown to rapidly decrease through a series of moderate-intensity convective storms in the first rainy season after the fire. Over 90% of the ash was delivered to the reservoir in the first year, and ash concentrations in suspended sediment were negligible after the second year. Delivery of the remainder of the fine sediment also declined rapidly after the first year despite the occurrence of higher-intensity storms in the second year. Fine sediment loads after five years remained significantly above prefire averages. Deposition of coarse-grained sediment was irregular in time and was associated with transport by snowmelt runoff of sediment stored along the upstream channel during short-duration summer floods. Coarse sediment delivery in the first four years was strongly correlated with snowmelt volume, suggesting a transport-limited system with abundant available sediment. Transport rates of coarse sediment declined in the fifth year, consistent with a transition to a more stable channel as the accessible sediment supply was depleted and the channel bed coarsened. Maximum impacts from ash and other fine-grained sediment therefore occurred soon after the fire, whereas the downstream impacts from coarse-grained sediment were attenuated by the more gradual process of bedload sediment transport.

Influence of sediment storage on downstream delivery of contaminated sediment

[1] Sediment storage in alluvial valleys can strongly modulate the downstream migration of sediment and associated contaminants through landscapes. Traditional methods for routing contaminated sediment through valleys focus on in-channel sediment transport but ignore the influence of sediment exchanges with temporary sediment storage reservoirs outside the channel, such as floodplains. In theory, probabilistic analysis of particle trajectories through valleys offers a useful strategy for quantifying the influence of sediment storage on the downstream movement of contaminated sediment. This paper describes a field application and test of this theory, using 137 Cs as a sediment tracer over 45 years (1952–1997), downstream of a historical effluent outfall at the Los Alamos National Laboratory (LANL), New Mexico. The theory is parameterized using a sediment budget based on field data and an estimate of the 137 Cs release history at the upstream boundary. The uncalibrated model reasonably replicates the approximate magnitude and spatial distribution of channel- and floodplain-stored 137 Cs measured in an independent field study. Model runs quantify the role of sediment storage in the long-term migration of a pulse of contaminated sediment, quantify the downstream impact of upstream mitigation, and mathematically decompose the future 137 Cs flux near the LANL property boundary to evaluate the relative contributions of various upstream contaminant sources. The fate of many sediment-bound contaminants is determined by the relative timescales of contaminant degradation and particle residence time in different types of sedimentary environments. The theory provides a viable approach for quantifying the long-term movement of contaminated sediment through valleys.

Robust decision analysis for environmental management of groundwater contamination sites

In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decisionsupport framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.