@article{fdi:010060846,
  title = {{D}ata-based comparison of frequency analysis methods : a general framework},
  author = {{R}enard, {B}. and {K}ochanek, {K}. and {L}ang, {M}. and {G}aravaglia, {F}. and {P}aquet, {E}. and {N}eppel, {L}. and {N}ajib, {K}. and {C}arreau, {J}ulie and {A}rnaud, {P}. and {A}ubert, {Y}. and {B}orchi, {F}. and {S}oubeyroux, {J}. {M}. and {J}ourdain, {S}. and {V}eysseire, {J}. {M}. and {S}auquet, {E}. and {C}ipriani, {T}. and {A}uffray, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}n abundance of methods have been developed over the years to perform the frequency analysis ({FA}) of extreme environmental variables. {A}lthough numerous comparisons between these methods have been implemented, no general comparison framework has been agreed upon so far. {T}he 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 {M}onte {C}arlo simulations or statistical testing. {T}his 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. {T}his framework is then applied to a case study involving 364 gauging stations in {F}rance, where 10 {FA} implementations are compared. {T}hese implementations correspond to the local, regional, and local-regional estimation of {G}umbel and generalized extreme value distributions. {R}esults show that reliability and stability indices are able to reveal marked differences between {FA} implementations. {M}oreover, 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. {T}he 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.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{W}ater {R}esources {R}esearch},
  volume = {49},
  numero = {2},
  pages = {825--843},
  ISSN = {0043-1397},
  year = {2013},
  DOI = {10.1002/wrcr.20087},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060846},
}