Home | Facilities | Projects | Data | Publications | Photos from the field | Map
CLAM age-depth modeling using monotonic splines.

The CLAM age-depth modeling R program fits age-depth relationships through a series of stratigraphically ordered radiocarbon dates. The original program includes several curve-fitting options, but no option in which a spline curve is constrained such that age increases monotonically with depth rather than "overshooting" at locations where there is a change in the slope. The monotonic spline option to CLAM was recommended by Trachsel and Telford 2017. The code was used in a paper by Schworer et al. 2016. Each of the three methods produces slightly different results. I have not (yet) explored the mathematical differences in these methods, and no doubt other methods exist as well. In my experience, the Bacon age-depth approach is difficult to apply to cases of sharp inflections in the sedimentation rate, and therefore prompting the need for a monotonic spline curve-fitting option.

  1. Install the R package stinepack. Install Clam version 2.2 as provided here. Note: do not install the clam package (version 2.3 or greater).
  2. This folder contains the new INTCAL20 calibration data set and a modified version of clam ("clam_mod_v1.R") as well as the original version of Clam.
  3. Load the CLAM program using the command source("clam_mod_v1.R"). This file substitutes for clam.R.

The program is unchanged from version 2.2, but with the addition of three new curve-fitting options:

  1. type=6. This uses a monotonic (or "constrained") spline. It is slower to run than the other options because the spline curve is not a compiled function; it is embedded in the Clam code. It uses an unpublished algorithm by CJC Kruger. The algorithm allows extrapolation to depths beyond the data.
  2. type=7. A stineman spline. Stineman, R. W. 1980. A consistently well-behaved method of interpolation. Creative Computing 6:54-57.
  3. type=8. The scaled stineman spline. Neither stineman spline can extrapolate beyond the upper-most or lower-most age control point.

K1D icon
K1D Version 1.2

Last update: July 2010

A program to analyze the dependence of two or more time event records using the Ripley K-function on one dimension. Examines multiple hypotheses regarding the form of relationship between events.
User's Guide (.pdf)
Mac OS X (.zip)
Windows (.zip)
[Example data file] [Example intensity file]
RealBASIC Project file (.zip)

R code for a limited set of analysis in K1d. K1D Manual for R. [Source code] [Example data (Utah fire history)]
Some studies that have implemented this software:
  • Gavin, D.G., F.S. Hu, K.P. Lertzman and P. Corbett. 2006. Weak climatic control of forest fire history during the late Holocene. Ecology 87:1722-1732.
  • Hu, F.S., L.B. Brubaker, D.G. Gavin, P.E. Higuera, J.A. Lynch, T.S. Rupp and W. Tinner. 2006. How climate and vegetation influence the fire regime of the Alaskan Boreal biome: The Holocene perspective. Mitigation and Adaptation Strategies for Global Change 11:829-846.
  • Bigler, C., D.G. Gavin, C. Gunning and T.T. Veblen. 2007. Drought induces lagged tree mortality in a subalpine forest in the Rocky Mountains. Oikos 116:1983-1994.
  • Schoennagel, T., T.T. Veblen, D. Kulakowski and A. Holz. 2007. Multidecadal climate variability and interactions among Pacific and Atlantic sea surface temperature anomalies affect subalpine fire occurrence, western Colorado (USA). Ecology.
  • Long, C.J., C. Whitlock and P.J. Bartlein 2007. Holocene vegetation and fire history of the Coast Range, western Oregon, USA. The Holocene 17:917-926.
  • Ali, A.A., C. Carcaillet and Y. Bergeron. 2009. Long-term fire frequency variability in the eastern Canadian boreal forest: the influences of climate vs. local factors. Global Change Biology 15:1230-1241.
  • Carcaillet, C., A.A. Ali, O. Blarquez, A. Genries, B. Mourier, and L. Bremond. 2009. Spatial variability of fire history in subalpine forests: From natural to cultural regimes. Ecoscience 16:1-12.
  • Hallett, D.J. and R.S. Anderson. 2010. Paleofire reconstruction for high-elevation forests in the Sierra Nevada, California, with implications for wildfire synchrony and climate variability in the late Holocene
    Quaternary Research 73: 180-190.

AET icon
AET Calculator

Last update: March 2007

A program for computing the annual climatic water balance using the modified Thornthwaite method.
User's Guide (.pdf)
Mac OS X (.zip)
Windows (.zip)

Example input data:

AET Calculator; R Code
This is the same code as used in the stand-alone program used above, however, it uses DAILY data and computes variables based on a daily time step.  Thus, it is more realistic, though it applies to daily data relationships that were developed for monthly data.  The effect of this has not been explored.   Knowledge of R language is needed.
Program file (last update: March 2009): AET_calculator.txt
Example data file: BTV_HCN_DAILY.csv

Water Balance and Climate Diagram interactive graphics; Shiny App
This interactive program uses the above methods to calculate a water balance climatology. It smooths monthly data using a method that preserves mean monthly precipitation and temperature.
GDD Calculator; R Code
Computes accumulated growing degree-days using daily climate data and the Baskerville-Emin sine-curve method.  Example input data same as above.
Program file (last update: March 2009): GDD_calculator.txt

charster icon
Charster; version 0.8.3

Last update: June 2006

A program for the exploratory analysis and archiving of lake sediment charcoal records.

More sophisticated analyses are implemented in the next-generation software by Phil Higuera: http://CharAnalysis.googlepages.com.  See the User's Guide for a comparison of these programs.

Bug list.
User's Guide (HTML)
Max OS X (.zip)
Windows (.zip)
Example raw data file for import (.txt)
Example Charster file (.zip) Installation notes (esp. for Windows users).