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.

As a generalization of the common assumption of exponential distribution of the exceedances in partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability‐weighted moments. The corresponding estimators for the T‐year event are given and approximate expressions for bias and variance of the estimators are derived in both cases. Using the mean square error of the T‐year event estimator as a performance index it is shown that the method of moments is preferable to the probability‐weighted moments. Maintaining the generalized Pareto distribution as the parent exceedance distribution the T‐year event is estimated assuming the exceedances to be exponentially distributed. For moderately long‐tailed exceedance distributions and small to moderate sample sizes it is found, by comparing mean square errors of the T‐year event estimators, that the exponential distribution is preferable to the correct generalized Pareto distribution despite the introduced model error and despite a possible rejection of the exponential hypothesis by a test of significance. For moderately short‐tailed exceedance distributions (with physically justified upper limit) the correct exceedance distribution should be applied despite a possible acceptance of the exponential assumption by a test of significance.

The transitions from foraging to farming and later to pastoralism in Stone Age Eurasia (c. 11-3 thousand years before present, BP) represent some of the most dramatic lifestyle changes in human evolution. We sequenced 317 genomes of primarily Mesolithic and Neolithic individuals from across Eurasia combined with radiocarbon dates, stable isotope data, and pollen records. Genome imputation and co-analysis with previously published shotgun sequencing data resulted in >1600 complete ancient genome sequences offering fine-grained resolution into the Stone Age populations. We observe that: 1) Hunter-gatherer groups were more genetically diverse than previously known, and deeply divergent between western and eastern Eurasia. 2) We identify hitherto genetically undescribed hunter-gatherers from the Middle Don region that contributed ancestry to the later Yamnaya steppe pastoralists; 3) The genetic impact of the Neolithic transition was highly distinct, east and west of a boundary zone extending from the Black Sea to the Baltic. Large-scale shifts in genetic ancestry occurred to the west of this "Great Divide", including an almost complete replacement of hunter-gatherers in Denmark, while no substantial ancestry shifts took place during the same period to the east. This difference is also reflected in genetic relatedness within the populations, decreasing substantially in the west but not in the east where it remained high until c. 4,000 BP; 4) The second major genetic transformation around 5,000 BP happened at a much faster pace with Steppe-related ancestry reaching most parts of Europe within 1,000-years. Local Neolithic farmers admixed with incoming pastoralists in eastern, western, and southern Europe whereas Scandinavia experienced another near-complete population replacement. Similar dramatic turnover-patterns are evident in western Siberia; 5) Extensive regional differences in the ancestry components involved in these early events remain visible to this day, even within countries. Neolithic farmer ancestry is highest in southern and eastern England while Steppe-related ancestry is highest in the Celtic populations of Scotland, Wales, and Cornwall (this research has been conducted using the UK Biobank resource); 6) Shifts in diet, lifestyle and environment introduced new selection pressures involving at least 21 genomic regions. Most such variants were not universally selected across populations but were only advantageous in particular ancestral backgrounds. Contrary to previous claims, we find that selection on the FADS regions, associated with fatty acid metabolism, began before the Neolithisation of Europe. Similarly, the lactase persistence allele started increasing in frequency before the expansion of Steppe-related groups into Europe and has continued to increase up to the present. Along the genetic cline separating Mesolithic hunter-gatherers from Neolithic farmers, we find significant correlations with trait associations related to skin disorders, diet and lifestyle and mental health status, suggesting marked phenotypic differences between these groups with very different lifestyles. This work provides new insights into major transformations in recent human evolution, elucidating the complex interplay between selection and admixture that shaped patterns of genetic variation in modern populations.

Multiproxy palaeoecological data for lake Dallund S0, Denmark, were synthesized to explore the link between changes in the terrestrial environment (from pollen, and sediment physical properties) with those in the aquatic environment (from diatom, macrofossil, zooplankton and Pediastrum data) since the introduction of agriculture c. 6000 years ago. The lake was relatively insensitive to catchment disturbance during the Neolithic (3870-1700 BC) and Early Bronze Age (1700-1000 BC) periods but was dramatically impacted by environmental changes associated with a major deforestation phase at the transition from the Late Bronze Age (1000-500 BC) to the Pre-Roman Iron Age (500 BC-AD 0). A major eutrophication of the lake took place as a result of a changing agricultural system and also the retting of flax and hemp during the Mediaeval period (AD 1050-1536). Analyses of the data sets representing the terrestrial and aquatic environments demonstrate that human activities over thousands of years have not only impacted and shaped the Danish landscape but have also played a major role in lake development.