Regionalisation of the “intensity-duration-frequency” curves in Northern Algeria
- 91 Downloads
Abstract
The flood events observed during last years in the urban areas are subject of main interest for quantification of the hydro-climatic risks and climatic change to the regional scales. The establishment of a statistical relationship between the intensities of intense rains and the recurrence of these events allows us to determine the dimensions of the works according to a previously defined level of risk. They constitute today a leading tool for various users. This work concerns the study of the maximum annual rains, recorded at 49 stations in the northern Algeria. The objectives of this work are to determine the estimators who are the “intensity-duration-frequency” curves and to extract from these whole of information the b Montana climatic parameter to be regionalized for the calculating the river flow and for the dimensioning of the networks of cleansing in the event of insufficiency of data. Different durations going from 15 min to 24 h are studied. We utilised the collocated co-kriging as multivariate estimation method for interpolation in order to yield the space distribution maps of b Montana climatic parameter, with the benefit of using spatially correlated secondary variables, such as the digital elevation model and the distance from the coastline that are known at any localisation. All features led to choose the digital elevation model as covariate for interpolating b Montana values, yielding a better regionalisation of the studied climatic parameter. The geostatistical handling of b Montana values strictly related to auxiliary variables that constitute physical factors overcomes the data shortage in planning, managing and preventing the rain flood risk.
Keywords
IDF b Montana Collocated co-kriging Maximum annual rain Climatic parameterNotes
Acknowledgments
The authors thank the Prof. Meddi Mohamed of ENSH (Algeria), Prof. Paolo Conti and Dott. Francesco Petrolo of CGT Centre for GeoTechnologies of University of Siena (Italy) for their support, attention and useful discussions. The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
References
- ANRH 2014 Données pluviométriques et pluviographiques. ANRH, Direction généraled’Alger, AlgérieGoogle Scholar
- Arnaud M, Emery X (2000) Estimation et interpolation spatiale. Hermes Science Publications, ParisGoogle Scholar
- Bouaïchi I, Touaïbia B, Dernouni F (2006) Approche méthodologique de calcul du débit pluvial en cas d’insuffisance de données. Cas de la région de Tipaza, In: Proceedings of AMHY-FRIEND de l’UNESCO International Workshop Hydrological Extremes "Observing and modelling exceptional floods and rainfalls", University of Calabria, Cosenza (Italy)Google Scholar
- Boukhelifa M (2011) Contribution à la modélisation de la relation « pluie débit » en absence de données hydrométriques : cas d’une zone urbaine (ville de Tipaza)Google Scholar
- Chilès J-P, Delfiner P (1999) Geostatistics: modeling spatial uncertainty, probability and statistics. John Wiley and Sons, New YorkCrossRefGoogle Scholar
- Chow V, Maidment D, Mays L (1988) Applied hydrology, 1st edn. McGraw-Hill Science/Engineering/Math, New YorkGoogle Scholar
- Cressie NE (1993) Statistics for spatial data, revised edition. Wiley & Sons Inc., New YorkGoogle Scholar
- Dagnellien P (1992) Statistiques théorique et appliquée, Tome 1. Presses agronomiques de Gembloux, BelgiqueGoogle Scholar
- Deliang C, Hans Weiteng C (2013) Using the Köppen classification to quantify climate variation and change: an example for 1901–2010. Environ Dev 6(2013):69–79Google Scholar
- Desbarats AJ, Logan CE, Hinton MJ, Sharpe DR (2002) On the kriging of water table elevations using collateral information from a digital elevation model. J Hydrol 255:25–38CrossRefGoogle Scholar
- Goovaerts P (1997) Geostatistics for natural resources evaluation, applied geostatistics series. Oxford University Press, New YorkGoogle Scholar
- Goovaerts P (1999) Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34:227–242CrossRefGoogle Scholar
- Guastaldi E, Del Frate AA (2012) Risk analysis for remediation of contaminated sites: the geostatistical approach. Environ Earth Sci 65(3):897–916CrossRefGoogle Scholar
- Guastaldi E, Baldoncini M, Bezzon G, Broggini C, Buso G, Caciolli A, Carmignani L, Callegari I, Colonna T, Dule K, Fiorentini G, Kaçeli Xhixha M, Mantovani F, Massa G, Menegazzo R, Mou L, Rossi Alvarez C, Strati V, Xhixha G, Zanon A (2013) A multivariate spatial interpolation of airborne gamma-ray data using the geological constraints. Remote Sens Environ 137:1–11CrossRefGoogle Scholar
- Hoeksema RJ, Clapp RB, Thomas AL, Hunley AE, Farrow ND, Dearstone KC (1989) Cokriging model for estimation of water table elevation. Water Resour Res 25:429–438CrossRefGoogle Scholar
- Hudson G, Wackernagel H (1994) Mapping temperature using kriging with external drift: theory and an example from Scotland. Int J Climatol 14:77–91CrossRefGoogle Scholar
- Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, San DiegoGoogle Scholar
- Kingumbi, A & Mailhot, A (2010) Courbes Intensité–Durée–Fréquence (IDF): comparaison des estimateurs des durées partielles et des maximums annuels. Intensity–Duration–Frequency (IDF) curves: comparison of annual maximum and partial duration estimators. Hydrol Sci J 55(2):162-176 https://doi.org/10.1080/02626660903545995
- Meylan P, Musy A (1999) Hydrologie fréquentielle. Editions *H*G*A*, BucarestGoogle Scholar
- Mohymont B, Demarée GR (2006) Intensity-duration-frequency curves for precipitation at Yangambi, Congo, derived by means of various models of Montana type. p 239–253Google Scholar
- Muller A (2006) Comportement asymptotique de la distribution des pluies extrêmes en France. Unité de recherche hydrologie- hydraulique, Université de MontpellierGoogle Scholar
- Musy A, Higy C (1998) Hydrologie appliquée. Editions *H*G*A*, BucarestGoogle Scholar
- Pei T, Qin CZ, Zhu AX, Yang L, Luo M, Li B, Zhou C (2010) Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods. Ecol Indic 10:610–619CrossRefGoogle Scholar
- Réméniéras G (1979) Hydrologie de l’ingénieur, Collection des études et recherches d’électricité de France. EyrollesGoogle Scholar
- Rivoirard J (2001) Which models for collocated cokriging? Math Geol 33(2):117–131CrossRefGoogle Scholar
- Roche PA, Miquel J, Gaume E (2012) Hydrologie quantitative: Processus, modèles et aide à la décision. Springer, Paris, p 389CrossRefGoogle Scholar
- Siegel S, Castellan NJJ (1988) Nonparametric statistics for the behavioral sciences, 2nd edn. McGraw-Hill, New YorkGoogle Scholar
- Taibi S, Meddi M, Mahé G, Assani A (2017) Relationships between atmospheric circulation indices and rainfall in Northern Algeria and comparison of observed and RCM-generated rainfall. Theor Appl Climatol 127(1–2):241–257. https://doi.org/10.1007/s00704-015-1626-4 CrossRefGoogle Scholar
- Touaibia B (2004) Manuel pratique d’hydrologie. Edition Madani. BlidaGoogle Scholar
- USGS (Ed.) (2004) Shuttle radar topography mission, 1 Arc second scene SRTM_GTOPO_u30_n040w020, Unfilled Unfinished 2.0, Global Land Cover Facility, University of Maryland, College ParkGoogle Scholar
- Wackernagel H (2003) Multivariate geostatistics—an introduction with applications. Springer, Berlin 387 ppGoogle Scholar
- Webster R, Oliver M (2001) Geostatistics for natural environmental scientists. John Wiley & Sons, Chichester 0–471–96553-7Google Scholar
- Xu W, Tran TT, Srivastava RM, Journel AG (1992) Integrating seismic data in reservoir modeling: the collocated cokriging alternative. In: SPE Annual Technical Conference Exhibition. 1992 Copyright 1992, Society of Petroleum Engineers Inc., Washington, D.CGoogle Scholar
- Yeung HY, Man C, Chan ST, Seed A (2014) Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis. Hydrol Sci J 7:1293–1307CrossRefGoogle Scholar