RAINFALL SYNTHESIS WITH SCANTY DATA

This paper presents mathematical models for seasonal and storm rainfall synthesis and establishes relationships to estimate model parameters when observed records are scarce. Seasonal rainfall is simulated by a linear model which maintains means, variances and serial correlation at each location, and cross-correlation between stations. Parameters are predicted in terms of longitude, latitude, elevation, slope of air streamlines, barrier height, and distance between stations. Storms are modelled hourly at a key station by a Markov process and linear relationships are used to transfer the simulated storms to other locations. Predictive relationships are developed for the parameters of the storm model as functions of seasonal characteristics. Application of the model shows it maintains the principal moments of hourly rainfall rates and of the amounts and durations of storms. The hourly rainfall generated by the model, when used as input to the Stanford watershed model, produces detailed streamflow series similar to observed runoff volumes and peak flows. /Author/

  • Corporate Authors:

    Elsevier Science

    Radarweg 29, P.O. Box 211
    1043NX AE Amsterdam,   Netherlands 
  • Authors:
    • Varas, E A
    • Linsley, R K
  • Publication Date: 1977-8

Media Info

  • Features: Figures; References; Tables;
  • Pagination: p. 235-249
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00164098
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 29 1977 12:00AM