This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY
[1] An abundance of methods have been developed over the years to perform the frequency analysis (FA) of extreme environmental variables. Although numerous comparisons between these methods have been implemented, no general comparison framework has been agreed upon so far. The objective of this paper is to build the foundation of a data-based comparison framework, which aims at complementing more standard comparison schemes based on Monte Carlo simulations or statistical testing. This framework is based on the following general principles: (i) emphasis is put on the predictive ability of competing FA implementations, rather than their sole descriptive ability measured by some goodness-of-fit criterion; (ii) predictive ability is quantified by means of reliability indices, describing the consistency between validation data (not used for calibration) and FA predictions; (iii) stability is also quantified, i.e., the ability of a FA implementation to yield similar estimates when calibration data change; and (iv) the necessity to subject uncertainty estimates to the same scrutiny as point estimates is recognized, and a practical approach based on the use of the predictive distribution is proposed for this purpose. This framework is then applied to a case study involving 364 gauging stations in France, where 10 FA implementations are compared. These implementations correspond to the local, regional, and local-regional estimation of Gumbel and generalized extreme value distributions. Results show that reliability and stability indices are able to reveal marked differences between FA implementations. Moreover, the case study also confirms that using the predictive distribution to indirectly scrutinize uncertainty estimates is a viable approach, with distinct FA implementations showing marked differences in the reliability of their uncertainty estimates. The proposed comparison framework therefore constitutes a valuable tool to compare the predictive reliability of competing FA implementations, along with the reliability of their uncertainty estimates.
Abstract:The annual peak flow series of Polish rivers are mixtures of summer and winter flows. As Part II of a sequence of two papers, practical aspects of applicability of seasonal approach to flood frequency analysis (FFA) of Polish rivers are discussed. Taking A Two-Component Extreme Value (TCEV 1) model as an example it was shown in the first part that regardless of estimation method, the seasonal approach can give profit in terms of upper quantile estimation accuracy that rises with the return period of the quantile and is the greatest for no seasonal variation. In this part, an assessment of annual maxima (AM) versus seasonal maxima (SM) approach to FFA was carried out with respect to seasonal and annual peak flow series of 38 Polish gauging stations. First, the assumption of mutual independence of the seasonal maxima has been tested. The smoothness of SM and AM empirical probability distribution functions was analysed and compared. The TCEV 1 model with seasonally estimated parameters was found to be not appropriate for most Polish data as it considerably underrates the skewness of AM distributions and upper quantile values as well. Consequently, the discrepancies between the SM and AM estimates of TCEV 1 are observed. Taking SM and TCEV 1 distribution, the dominating season in AM series was confronted with predominant season for extreme floods. The key argument for presumptive superiority of SM approach that SM samples are more statistically homogeneous than AM samples has not been confirmed by the data. An analysis of fitness to SM and AM of Polish datasets made for seven distributions pointed to Pearson (3) distribution as the best for AM and Summer Maxima, whereas it was impossible to select a single best model for winter samples. In the multi-model approach to FFA, the tree functions, i.e., Pe(3), CD3 and LN3, should be involved for both SM and AM. As the case study, Warsaw gauge on the Vistula River was selected. While most of AM elements are here from winter season, the prevailing majority of extreme annual floods are the summer maxima. The upper quantile estimates got by means of classical annual and two-season methods happen to be fairly close; what's more they are nearly equal to the quantiles calculated just for the season of dominating extreme floods.
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