Abstract. Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value distribution for annual maxima. The performance of the two models in terms of the uncertainty of the T-year event estimator is evaluated in the cases of estimation with, respectively, the maximum likelihood (ML) method, the method of moments (MOM), and the method of probability weighted moments (PWM). In the case of ML estimation, the PDS model provides the most efficient T-year event estimator. In the cases of MOM and PWM estimation, the PDS model is generally preferable for negative shape parameters, whereas the AMS model yields the most efficient estimator for positive shape parameters. A comparison of the considered methods reveals that in general, one should use the PDS model with MOM estimation for negative shape parameters, the PDS model with exponentially distributed exceedances if the shape parameter is close to zero, the AMS model with MOM estimation for moderately positive shape parameters, and the PDS model with ML estimation for large positive shape parameters. Since heavy-tailed distributions, corresponding to negative shape parameters, are far the most common in hydrology, the PDS model generally is to be preferred for at-site quantile estimation.

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Abstract:Basic concepts such as conditional probability distributions, conditional return periods, and joint return periods are important to understand and to interpret multivariate hydrological events such as floods and storms. However, these concepts are not well documented in the open literature. This paper assembles and clarifies these concepts, and illustrates their practical utility. Relationships between joint return periods and univariate return periods are also derived. These concepts and relationships are demonstrated by applying a bivariate extreme value distribution to represent the joint distribution of flood peak and volume from an actual basin.

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This paper presents an assessment of the operational system used by the Meteorological Service of Canada for producing near-real-time precipitation analyses over North America. The Canadian Precipitation Analysis (CaPA) system optimally combines available surface observations with numerical weather prediction (NWP) output in order to produce estimates of precipitation on a 15-km grid at each synoptic hour (0000, 0600, 1200, and 1800 UTC). The validation protocol used to assess the quality of the CaPA has demonstrated the usefulness of the system for producing reliable estimates of precipitation over Canada, even in areas with few or no weather stations. The CaPA is found to be better in autumn, spring, and winter than in summer. This is because of the difficulty in correctly producing convective precipitation in the NWP because of the low spatial resolution of the meteorological model. An investigation of the quality of the precipitation analyses in the 15 terrestrial ecozones of Canada indicates the need to have a sufficient number of observations (at least ~1.17 stations per 10 000 km2) in order to produce a precipitation analysis that is significantly better than the raw NWP product. Improvements of the CaPA system by including provincial networks as well as radar and satellite information are expected in the future.