VHP Scope

James R. Wallis (1928-2016)

Wallis_1.jpegJim Wallis studied the stochastic structure of streamflow over a range of time scales. He was born in Montreal, grew up in wartime London, and returned to Canada in 1946 to study forestry at the University of New Brunswick. He later did graduate work in forestry at Oregon State University and UC Berkeley. While a graduate student, he worked at Pacific Gas and Electric in San Francisco, where he was motivated to learn about operations research and the use of digital computers. He joined IBM’s Thomas J Watson Research Center in Yorktown Heights, New York in 1966. Notwithstanding that his Ph.D. research had dealt with forest erosion, he began to interact with Benoit Mandelbrot, who was working on a theory of fractals. His interest was piqued by H.E. Hurst’s work on the Nile River, dealing with the long-term persistence of its annual flows. In the late 1960s, he and Mandelbrot developed synthetic streamflow models that were able to reproduce the behavior Hurst had observed in observations. In the mid-1970s, and USGS scientists Nick Matalas and Jim Slack investigated the structure of regional variations in flood frequency. In the 1980s, working with J.R.M. Hosking, he developed regional estimation procedures that lead to better estimates of the parameters of distributions like the Generalized Extreme Value. A 1997 book by Hosking and Wallis provides a complete treatise on regional frequency analysis that remains the key reference on the subject.   He was President of the Hydrology Section from 1980-82, during which he initiated the Horton Research Grants, and helped to reform AGU’s procedures for selecting Fellows.


Lettenmaier, D. P., P. Enda O'Connell, E. Todini and E. F. Wood (2016), James R. Wallis, 1928-2016, AGU Hydrology Newsletter July 2016, 23-25.

Wallis, J.R., (2010), The education of Jim Wallis, https://hydrology.agu.org/wp-content/uploads/sites/19/2016/02/The-Education-of-Jim-Wallis.pdf

***Please send your suggestions, additions, and corrections to Efi Foufoula-Georgiou.*** 

List of Publications

  1. Wallis, J. R. (1965), Multivariate statistical methods in hydrology—A comparison using data of known functional relationship,Water Resources Research1(4), 447–461. [PDF]
  2. Wallis, J. R. (1967), When is it safe to extend a prediction equation?—An answer based upon factor and discriminant function analysis,Water Resources Research3(2), 375–384. [PDF]
  3. Wallis, J. R. (1968), Factor Analysis in Hydrology—An Agnostic View,Water Resources Research4(3), 521–527. [PDF]
  4. Mandelbrot, B. B., and J. R. Wallis (1968), Noah, Joseph and Operational Hydrology, Water Resources Research, 4(5), 909-918. [PDF]
  5. Mandelbrot, B. B., and J. R. Wallis (1969), Some long-run properties of geophysical records, Water Resources Research, 5(2), 321-340. [PDF]
  6. Mandelbrot, B. B., and J. R. Wallis (1969), Computer experiments with fractional Gaussian noises: Part 1, averages and variances, Water Resources Research, 5(1), 228-241. [PDF]
  7. Mandelbrot, B. B. and J. R. Wallis (1969), Computer experiments with fractional Gaussian noises: Part 2, rescaled ranges and spectra, Water Resources Research, 5(1), 242-259. [PDF]
  8. Mandelbrot, B. B., and J. R. Wallis (1969), Computer experiments with fractional Gaussian noises: Part 3, mathematical appendix, Water Resources Research, 5(1), 260-267. [PDF]
  9. Mandelbrot, B. B. and J. R. Wallis (1969), Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence, Water Resources Research, 5(5), 967-988. [PDF]
  10. Wallis, J. R. and N. C. Matalas (1970), Small sample properties of H and K—Estimators of the Hurst coefficient h, Water Resources Research, 6(6), 1583-1594. [PDF]
  11. Matalas, N. C. and J. R. Wallis (1971), Statistical properties of multivariate fractional noise processes, Water Resources Research, 7(6), 1460-1468. [PDF]
  12. Wallis, J. R., and N. C. Matalas (1971), Correlogram Analysis Revisited,Water Resources Research7(6),1448–1459. [PDF]
  13. Wallis, J. R., and N. C. Matalas (1972), Sensitivity of reservoir design to the generating mechanism of inflows,Water Resources Research8(3), 634–641. [PDF]
  14. Botkin, D. B., J. F. Janak, and J. R. Wallis (1972), Some ecological consequences of a computer model of forest growth, The Journal of Ecology, 849-872. [LINK]
  15. Botkin, D. B., J. F. Janak, and J. R. Wallis (1972), Rationale, limitations, and assumptions of a northeastern forest growth simulator, IBM Journal of Research and Development, 16(2), 101-116. [LINK]
  16. Wallis, J. R., and P. E. O'Connell (1972), Small sample estimation of ρ1,Water Resources Research8(3),707–712. [PDF]
  17. Matalas, N. C., and J. R. Wallis (1973), Eureka! It fits a Pearson type: 3 distribution, Water Resources Research, 9(2), 281-289. [PDF]
  18. Matalas, N. C., J. R. Slack, and J. R. Wallis (1975), Regional skew in search of a parent, Water Resources Research, 11(6), 815-826. [PDF]
  19. Slack, J. R., J. R. Wallis and N. C. Matalas (1975), On the value of information to flood frequency analysis, Water Resources Research, 11(5), 629-647. [PDF]
  20. Wallis, J. R., N. C. Matalas, and J. R. Slack (1976), Effect of sequence length n on the choice of assumed distribution of floods,Water Resources Research12(3), 457–471. [PDF]
  21. Wallis, J. R., N. C. Matalas, and J. R. Slack (1977), Apparent regional skew, Water Resources Research, 13(1), 159-182. [PDF]
  22. Landwehr, J. M., N. C. Matalas, and J. R. Wallis (1978), Some comparisons of flood statistics in real and log space, Water Resources Research, 14(5), 902-920. [PDF]
  23. Greenwood, J. A., J. M. Landwehr, N. C. Matalas, and J. R. Wallis (1979), Probability weighted moments: definition and relation to parameters of several distributions expressable in inverse form, Water Resources Research, 15(5), 1049-1054. [PDF]
  24. Landwehr, J. M., N. C. Matalas, and J. R. Wallis (1979), Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles, Water Resources Research, 15(5), 1055-1064. [PDF]
  25. Landwehr, J. M., N. C. Matalas, and J. R. Wallis (1979), Estimation of parameters and quantiles of Wakeby distributions: 1. Known lower bounds, Water Resources Research, 15(6), 1361-1372. [PDF]
  26. Landwehr, J. M., N. C. Matalas, and J. R. Wallis (1979), Estimation of parameters and quantiles of Wakeby Distributions: 2. Unknown lower bounds, Water Resources Research15(6), 1373–1379. [PDF]
  27. Landwehr, J. M., N. C. Matalas, and J. R. Wallis (1980), Quantile estimation with more or less floodlike distributions, Water Resources Research16(3), 547–555. [PDF]
  28. Hosking, J. R. M., J. R. Wallis and E. F. Wood (1985), Estimation of the generalized extreme-value distribution by the method of probability-weighted moments, Technometrics, 27(3), 251-261. [LINK]
  29. Hosking, J. R. M., J. R. Wallis and E. F. Wood (1985), An appraisal of the regional flood frequency procedure in the UK Flood Studies Report, Hydrological Sciences Journal, 30(1), 85-109. [LINK]
  30. Wallis, J. R., and E. F. Wood (1985), Relative accuracy of log Pearson III procedures, Journal of Hydraulic Engineering, 111(7), 1043-1056. [LINK]
  31. Hosking, J. and J. R. Wallis (1986), The value of historical data in flood frequency analysis, Water Resources Research, 22(11), 1606-1612. [PDF]
  32. Hosking, J. R. M., and J. R. Wallis (1986), Paleoflood hydrology and flood frequency analysis, Water Resources Research, 22(4), 543-550. [PDF]
  33. Hosking, J. R., and J. R. Wallis (1987), Parameter and quantile estimation for the generalized Pareto distribution, Technometrics, 29(3), 339-349. [LINK]
  34. Lettenmaier, D. P., J. R. Wallis and E. F. Wood (1987), Effect of regional heterogeneity on flood frequency estimation, Water Resources Research, 23(2), 313-323. [PDF]
  35. Hosking, J. R. M., and J. R. Wallis (1988), The effect of intersite dependence on regional flood frequency analysis, Water Resources Research, 24(4), 588-600. [PDF]
  36. Wallis, J. R., D. P. Lettenmaier, and E. F. Wood (1991), A daily hydroclimatological data set for the continental United States,Water Resources Research27(7), 1657–1663. [PDF]
  37. Hosking, J. R. M., and J. R. Wallis (1993), Some statistics useful in regional frequency analysis, Water Resources Research, 29(2), 271-281. [PDF]
  38. Guttman, N. B., J. R. M. Hosking, and J. R. Wallis (1993), Regional precipitation quantile values for the continental United States computed from L-moments, Journal of Climate, 6(12), 2326-2340. [LINK]
  39. Lettenmaier, D. P., E. F. Wood, and J. R. Wallis (1994), Hydro-climatological trends in the continental United States, 1948-88, Journal of Climate, 7(4), 586-607. [LINK]
  40. Handcock, M. S., and J. R. Wallis (1994), An approach to statistical spatial-temporal modeling of meteorological fields, Journal of the American Statistical Association, 89(426), 368-378. [LINK]
  41. Hosking, J. R. M., and J. R. Wallis (1995), A Comparison of Unbiased and Plotting‐Position Estimators of L Moments, Water Resources Research, 31(8), 2019-2025. [PDF]


  1. Hosking, J.R.M. and Wallis, J.R., 2005. Regional frequency analysis: an approach based on L-moments. Cambridge University Press.