DELTAFORCE geoclutter

Introduction
LONG-TERM GOALS
Compile geological data and develop methods to predict the distribution and properties of features hypothesized to be responsible for sonar geoclutter. Geological structures just beneath the seafloor, such as steep-walled channels, may have high-angle reflecting surfaces that can return false sonar alarms to ships operating in the littoral zone. The major goal is to contribute to the reduction or mitigation of geologic clutter observed on fleet sonar systems.

Two issues define the problem.

Landscape forming issue: In area ‘x’, can the Navy expect geoclutter features and if so what are their sonar characteristics, i.e. channel orientation.
Landscape burial issue: If geoclutter features are expected in area ‘x’, will the features be exposed or buried. Areas of low interest to the Navy include locations where Holocene deposits are thick. Areas of high interest to the Navy include locations where Holocene deposits are thin thereby allowing for the shallow burial of Pleistocene topography.

You can also download this article as a file.
The previous article published on this page is also still downloadable as file.

Involved institutes and People
PI: James P.M. Syvitski
INSTAAR, Univ. of Colorado, 1560 30^th St., Boulder CO, 80309-0450
phone: (303)492-7909 fax: (303)492-3287
Co-I: Scott Peckham
INSTAAR, Univ. of Colorado, 1560 30^th St., Boulder CO, 80309-0450
phone: (303)492-6752 fax: (303)492-3287

Related Projects

ONR EuroSTRATAFORM: Modeling the Effect of Climatic and Human Impacts on Margin Sedimentation.
ONR Seabed Uncertainty: Seabed Variability and its Influence on Acoustic Prediction Uncertainty.
NSF CSDMS: Community Sedimentary Model Science Plan for Sedimentology and Stratigraphy.

Initial Proposal
OBJECTIVES

Define the character of different kinds of buried channels (size, shape, properties).
Define the spatial distribution of these buried channels (river, tidal, hyperpycnal).
Develop a global atlas of candidate geoclutter features and their characteristics.
Develop and merge global databases of pertinent geological and oceanographic data.
Develop predictive models and apply to margins of interest. Test predictive models in a known geoclutter rich area.
Share and merge these databases, models and results with those in the Geoclutter Research Group working on tracking algorithms.

APPROACH

Compile a global database of pertinent geological and oceanographic data, for use as initial inputs and constraints for sediment flux models (HydroTrend and SedFlux).
Measure and analyze terrain attributes. Perform a comprehensive analysis of real and simulated elevation grids using RiverTools® and other GIS software. Calculate the geometric and statistical characteristics of landforms and how these characteristics vary from one geologic setting to another.
Classify terrain from geologic information. Classify “terrain types” in terms of the initial and boundary conditions (e.g. geology, erosion rates, excess rain rates) that produced the terrain types, using physics-based landform models.
Determine the burial depth potential of low-sea level produced topography. Develop simple scaling relationships for deposition rate as a function of sediment input rates from rivers, wave and current conditions, and shelf geometry. Refine these bulk estimates with more detailed consideration of the nature of sediment delivery to the shelf (e.g., episodic storm-driven flooding vs. seasonal snowmelt flooding; the role of estuaries) and sediment redistribution, bypassing and deposition on the shelf (e.g., the long-term manifestation of short-term, episodic, storm-driven transport on the shelf).
Model the flux of sediment to and across continental shelves. Use process-based models (HydroTrend) to obtain a detailed consideration of the nature of sediment delivery to the shelf and sediment redistribution, bypassing and deposition on the shelf.

Annual Reports
WORK COMPLETED

Compiled global datasets (e.g. Fig. 1 & 2) on wave height, period, wind force and direction (NOAA's WaveWatch III), basin temperature (University of Delaware), precipitation (University of Delaware) for use as inputs to INSTAAR sediment flux models. The data were analyzed in terms of intra-annual variability, intra-annual variability for the world (0.5° x 0.5°).

Figure 1. Coefficient of Variation of Discharge for the month of December, based on daily data for a 34 yr period (1960-1994: Data from University of New Hampshire). Scale is from 0 to 55 (dimensionless).

Figure 2. Coefficient of Variation of Precipitation for the month of September based, on daily data for a 29 yr period (1970-1999: data from University of Delaware). Scale is from 0 to 5.5 (dimensionless).
Predicted both the monthly and yearly discharge of the modern river systems (Fig. 3) as an aid to understanding where shallow burial of paleo river channels could be considered likely. The modeling employed both the QRT and ART models developed by Syvitski et al., 2003, and was carried out in conjunction with scientists from the University of New Hampshire.

Figure 3. Global map of the world showing sediment load in MT/yr predicted for ~6000 river basins based on the QRT model of Syvitski et al., 2003

Where the discharge is largely natural (e.g. Fraser River) the QRT or ART models works well at predicting the monthly sediment flux (Fig. 4). Where the river flow is regulated through reservoir trapping (e.g. Mississippi, Po), the models under-predicts the sediment load (e.g. since 1960 in the Mississippi). Since mine burial is predicated on burial by sediments discharge before human perturbation, then the models prove to be a verified way to predict burial of Pleistocene channels on continental margins.

Figure 4. QRT model (Syvitski et al., 2003) simulations of three rivers (top panel: Mississippi (1949-1980), middle panel: Fraser (1965-1975), and bottom panel: Po (1932-1987)). In each panel, the monthly mean and interannual standard deviation of the predicted discharge (UNH global water cycle model) is compared with the observed discharge (USGS, WSC, and IGM sources), the monthly mean and interannual standard deviation of the predicted sediment load (QRT model) is compared with the observed load (USGS, WSC, and IGM sources). The monthly simulations are also compared to observations as a monthly time series in the right panels.
Analyzed the time-varying river basin catchments area with 200yr intervals for the East coast of the U.S. across the last 21,000 years using isostatic/eustatic (dynamic) adjustments of ice sheet load and changes in sea level, both due to fluctuations in ice volume.
Developed inverse methods to estimate the key parameters that appear in fluvial landscape evolution models from observed elevation data (DEMs). Among these key parameters are the geomorphically-effective rainrate, R, the area-discharge exponent, θ, and the slope and area exponents, m and n. The underlying idea is that if we can determine what these parameters are for a coastline segment using observational data and statistical estimation methods, then we can use them as a proxy for the values that led to the formation of the mud-buried paleotopography offshore. The relations of hydraulic geometry can then be used to compute channel geometric characteristics from the coarser-scale fluvial landscape characteristics.
Developed a new fluvial landscape evolution model with improved algorithms as a step toward being able to generate larger landscape grids in a reasonable amount of time. It was found that the existing models available to us were prohibitively slow for creating grids large enough to allow extraction of the required geometric measurements. In order to effectively map the parameter space of a fluvial landscape model, we need a model that can run different scenarios with different parameters (for a large grid) as rapidly as possible.

RESULTS
Areas of high sediment flux (i.e. > 50MT/yr) should not be considered as sites where shallow burial of Pleistocene channels would not produce sonar geo-clutter: the channels are too deeply buried. Such coasts are found off the Amazon, Magdalena, Parana, and Orinoco rivers of South America, the Mississippi, Eel, Yukon, Fraser and Cooper rivers of North America, the Ganges, Mekong, Red and Yellow rivers of South Asia, and off many of the Islands comprising the Philippines, Indonesia and Taiwan. Where the dispersal energy is high by tides (e.g. Yangze, Fly, Indus rivers), by currents (e.g. Adriatic, Gulf of Lions), or by waves (e.g. much of the coast of India) the chance of sonar clutter from shallow buried channels is considered to be higher.

We have developed methods to estimate several of the key input parameters required by fluvial landscape evolution models from observed landscape data as an inverse problem. Starting from the physically-based equations that govern fluvial landscape evolution, we have derived general scaling relations and solutions for various cases that expose the manner in which observable features such as longitudinal profiles depend on the parameters in the equations. Our recent results show how observed longitudinal profiles and generalized Horton plots of channel slope vs. area can be combined with theoretical predictions to estimate the values of R, θ, m and n for a given fluvial landscape. The method involves fitting curves of the types predicted by theory to observational data, and then back-calculating these four key parameters by matching the statistical best-fit parameters to their theoretically predicted values (which are functions of the key parameters). This shows that these physical parameters are recorded in the geometric details of the fluvial landscape, such as the longitudinal profile for the main channel. Code was developed to extract the main channel for each pixel along a given coastline and to then estimate the key parameters above using statistical inverse methods. This makes it possible to simulate mud-buried paleotopography just offshore of various locations along a coastline in such a way that it will have the same statistical properties as the adjacent-shore subaerial topography. That is, the estimated parameters can be used as input to any fluvial landscape evolution model to simulate topography with the appropriate statistical properties, and this topography can be ingested by an acoustic (sonar clutter) model.

IMPACT/APPLICATIONS
The acoustic community and geophysical community including modelers, must determine more specifically, the exact nature of the sonar clutter, and how much of the clutter is from features within the channels or the channel walls, to further support this effort.

Papers

PUBLICATIONS

Morehead, M.D., Syvitski, J.P.M., and Hutton, E.W.H. and Peckham, S.D., 2003. Modeling the inter-annual and intra-annual variability in the flux of sediment in ungauged river basins. Global and Planetary Change, 39: 95-110. [in press, refereed]
Peckham, S.D. 2003, Mathematical modeling of landforms: Optimality and steady-state solutions, proceedings volume of a meeting of the Japanese Geomorphological Union, in Tokyo, Japan, 2002. [published].
Peckham, S. D., 2003. Fluvial landscape models and catchment-scale sediment transport. Global and Planetary Change, 39(1-2): 31-51.[in press, refereed]
Syvitski, J.P.M., 2003. Sediment Fluxes and Rates of Sedimentation. In: G.V. Middleton (Ed.) Encyclopedia of Sediments and Sedimentary Rocks. Kluwer Academic Publishers, Dordrecht, Netherlands, p. 600-606. [in press, refereed]
Syvitski, J.P.M. 2003. Plumes. In: A.S. Goudie (Ed.) Encyclopedia of Geomorphology. Routledge. [in press, refereed]
Syvitski, J.P.M., 2003. Supply and flux of sediment along hydrological pathways: Research for the 21st Century. Global and Planetary Change. [in press, refereed]
Syvitski, J.P.M., Kettner, A.J., Peckham, S.D. and Kao, S.-J., 2004. Predicting the Flux of Sediment to the Coastal Zone:Application to the Lanyang watershed, northern Taiwan Hydrological Processes. Journal of Coastal Research, 20(4). [in press, refereed]
Syvitski, J.P.M., Peckham, S.D., Hilberman, R.D., and Mulder, T., 2003. Predicting the terrestrial flux of sediment to the global ocean: A planetary perspective. Sedimentary Geology, 162: 5-24. [published, refereed]
Syvitski, J.P.M., 2003. The influence of climate on the flux of sediment to the coastal ocean. Proceedings of OCEANS 2003, San Diego, Holland Publ., p. 496-502. [published, refereed]

Presentations

Presentations of meeting at INSTAAR, 28 February - 1 March 2003.

James Syvitski Overview pdf file file of overview

James Syvitski Channel burial file of Channel burial

Alan Howard Fluvial Erosion of Continental Shelves file of Fluvial Erosion

Sergio Fagherazzi Barrier Island file of Barrier Island

Scott Peckham Channel forming file of channel forming

Charles Holland by James Syvitski Observation of geoclutter pdf file file of Observation of geoclutter

David Mixon Sea level change file of sea level change

Irina Overeem Quantifying stratigraphic variability,
case study, New Jersey Shelf file of Quantifying stratigraphic
variability

Pat Wiberg Shelf sediment transport file of Shelf sediment transport

J.P. Walsh Channel Databases pdf file file of Channel-Databases

Chris Jenkins dbSEABED file of dbSEABED

Animations