PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The added value of spatially distributed meteorological data for simulating hydrological processes in a small Mediterranean catchment

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of this paper was to demonstrate the added value of the spatial distribution of rainfall and potential evapotranspiration (PE) in the prediction of the discharge for a small Mediterranean catchment located in the Medjerda basin in Tunisia, i.e. the Raghay. We compare therefore the performance of a conceptual hydrological model available in the ATHYS platform, using global and spatial distributed input data. The model was implemented in two diferent ways. The frst implementation was in a spatially distributed mode, and the second one was in a non-distributed lumped mode by using spatially averaged data weighed with a Thiessen-interpolated factor. The performance of the model was analysed for the distributed mode and for the lumped mode with a cross-validation test and through several modelling evaluation criteria. Simultaneously, the impact of the spatial distribution of meteorological data was assessed for the two cases when estimating the model parameters, the fow and water amounts, and the fow duration curves. The cross-validation of the split-sample test shows a preference for the spatially distributed model based on accuracy criteria and graphical comparison. The distributed mode required, however, more simulation time. Finally, the results reported for the Raghay indicated that the added value of the spatial distribution of rainfall and PE is not constant for the whole series of data, depending on the spatial and temporal variability of climate data over the catchment that should be assessed prior to the modelling implementations.
Czasopismo
Rocznik
Strony
133--153
Opis fizyczny
Bibliogr. 56 poz.
Twórcy
autor
  • U-R:Gestion Durable des Ressources en Eau et en Sol (GDRES), High School of Engineering of Medjez el Bab (ESIM), University of Jendouba, Jendouba, Tunisia
  • National Agronomic Institute of Tunisia (INAT), University of Carthage, Tunis, Tunisia
  • National Agronomic Institute of Tunisia (INAT), University of Carthage, Tunis, Tunisia
  • U-R:Gestion Durable des Ressources en Eau et en Sol (GDRES), High School of Engineering of Medjez el Bab (ESIM), University of Jendouba, Jendouba, Tunisia
  • Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
  • Centre for Development and Environment (CDE), University of Bern, Bern, Switzerland
  • HydroSciences Montpellier, Institut de Recherche pour le Développement (IRD), Montpellier, France
Bibliografia
  • 1. Ajami NK, Gupta H, Wagener T, Sorooshian S (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. J Hydrol 298:112–135. https://doi.org/10.1016/J.JHYDROL.2004.03.033
  • 2. Andréassian V, Oddos A, Michel C, Anctil F, Perrin C, Loumagne C (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall–runoff models: a theoretical study using chimera watersheds. Water Resour Res. https://doi.org/10.1029/2003WR002854
  • 3. Apip Sayama T, Tachikawa Y, Takara K (2012) Spatial lumping of a distributed rainfall–sediment–runoff model and its effective lumping scale. Hydrol Process 26:855–871. https://doi.org/10.1002/hyp.8300
  • 4. Bao H, Wang L, Zhang K, Li Z, Wang L (2017) Application of a developed distributed hydrological model based on the mixed runoff generation model and 2D kinematic wave flow routing model for better flood forecasting. Atmos Sci Lett 18:284–293. https://doi.org/10.1002/asl.754
  • 5. Bargaoui Z, Hamouda D, Houcine A (2008) Modélisation pluie-débit et classification hydroclimatique. Rev des Sci l’eau 21:233–245. https://doi.org/10.7202/018468ar
  • 6. Bentura PLF, Michel C (1997) Flood routing in a wide channel with a quadratic lag-and-route method. Hydrol Sci J 42(2):169–189
  • 7. Botter G, Zanardo S, Porporato A, Rodriguez-Iturbe I, Rinaldo A (2008) Ecohydrological model of flow duration curves and annual minima. Water Resour Res. https://doi.org/10.1029/2008WR006814
  • 8. Bouvier C, Marchandise A, Brunet P, Crespy A (2008) Un modèle pluie-débit distribué événementiel parcimonieux pour la prédétermination et la prévision des crues éclair en zone méditerranéenne. Application au bassin du Gardon d’Anduze. IWRA congress 2008, p 10. https://www.iwra.org/congress/2008/resource/authors/abs811_article.pdf. Accessed 23 June 2018
  • 9. Bouvier C, Crespy A, L’Aour-Dufour A, Crès FN, Desclaux F, Marchandise A (2013) Distributed hydrological modeling—the ATHYS platform. Modeling software. Wiley, Hoboken, pp 83–100
  • 10. Breuer L, Huisman JA, Willems P, Bormann H, Bronstert A, Croke B, Frede H, Gräff T, Hubrechts L, Jakeman A, Kite G, Lanini J, Leavesley G, Lettenmaier D, Lindström G, Seibert J, Sivapalan M, Viney N (2009) Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: model intercomparison with current land use. Adv Water Resour 32:129–146. https://doi.org/10.1016/j.advwatres.2008.10.003
  • 11. Brocca L, Melone F, Moramarco T, Wagner W, Naeimi V, Bartalis Z, Hasenauer S (2010) Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Earth Syst Sci 145194:1881–1893. https://doi.org/10.5194/hess-14-1881-2010
  • 12. Brulebois E, Ubertosi M, Castel T, Richard Y, Sauvage S, Sanchez Perez JM, Le Moine N, Amiotte-Suchet P (2018) Robustness and performance of semi-distributed (SWAT) and global (GR4J) hydrological models throughout an observed climatic shift over contrasted French watersheds. Open Water J 5(1):41–56. https://scholarsarchive.byu.edu/openwater/vol5/iss1/4
  • 13. Castellarin A, Galeati G, Brandimarte L, Montanari A, Brath A (2004) Regional flow-duration curves: reliability for ungauged basins. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2004.08.005
  • 14. Coustau M, Bouvier C, Borrell-Estupina V, Jourde H (2012) Flood modelling with a distributed event-based parsimonious rainfall–runoff model: case of the karstic Lez river catchment. Nat Hazards Earth Syst Sci 12:1119–1133. https://doi.org/10.5194/nhess-12-1119-2012
  • 15. Dakhlaoui H, Ruelland D, Tramblay Y, Bargaoui Z (2017) Evaluating the robustness of conceptual rainfall–runoff models under climate variability in northern Tunisia. J Hydrol 550:201–217. https://doi.org/10.1016/J.JHYDROL.2017.04.032
  • 16. Dile YT, Srinivasan R (2014) Evaluation of CFSR climate data for hydrologic prediction in data-scarce watersheds: an application in the blue nile river basin. J Am Water Resour Assoc 50:1226–1241. https://doi.org/10.1111/jawr.12182
  • 17. Duan Q, Gupta HV, Sorooshian S, Rousseau A, Turcotte R (2003) Calibration of watershed models. American Geophysical Union, Washington
  • 18. Fang X, Thompson DB, Cleveland TG et al (2008) Time of concentration estimated using watershed parameters determined by automated and manual methods. J Irrig Drain Eng 134:202–211. https://doi.org/10.1061/(ASCE)0733-9437(2008)134:2(202)
  • 19. Fuka DR, Walter MT, Macalister C, Degaetano A, Steenhuis T, Easton Z (2014) Using the climate forecast system reanalysis as weather input data for watershed models. Hydrol Process. https://doi.org/10.1002/hyp.10073
  • 20. Gábor A, Villaverde AF, Banga JR (2017) Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC Syst Biol 11:54. https://doi.org/10.1186/s12918-017-0428-y
  • 21. Gader K, Gara A, Slimani M, Mahjoub MR (2015) Study of spatio-temporal variability of the maximum daily rainfall. In: 2015 6th International conference on modeling, simulation, and applied optimization (ICMSAO). IEEE, pp 1–6
  • 22. Ganora D, Claps P, Laio F, Viglione A (2009) An approach to estimate nonparametric flow duration curves in ungauged basins. Water Resour Res. https://doi.org/10.1029/2008WR007472
  • 23. Gara A, Gader K, Bergaoui M, Mahjoub MR (2015) Assessment of the hydrological response of the watershed through a distributed physically-based modeling for extreme events: application in the Raghay catchment (Medjerda) (Northern Tunisia). In: 6th international conference on modeling, simulation, and applied optimization, ICMSAO 2015. IEEE, pp 1–6
  • 24. Ghorbel A (1976) Etude hydrologique de l’oued Rarai. 26
  • 25. Hawkins RH (1993) Asymptotic determination of runoff curve numbers from data. J Irrig Drain Eng 119:334–345. https://doi.org/10.1061/(ASCE)0733-9437(1993)119:2(334)
  • 26. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423. https://doi.org/10.2307/2529430
  • 27. Ibrahim B, Wisser D, Barry B, Fowe T, Aduna A (2015) Hydrological predictions for small ungauged watersheds in the Sudanian zone of the Volta basin in West Africa. J Hydrol Reg Stud 4:386–397. https://doi.org/10.1016/J.EJRH.2015.07.007
  • 28. Khakbaz B, Imam B, Hsu K, Sorooshian S (2012) From lumped to distributed via semi-distributed: calibration strategies for semi-distributed hydrologic models. J Hydrol 418–419:61–77. https://doi.org/10.1016/J.JHYDROL.2009.02.021
  • 29. Klemeš V (1986) Operational testing of hydrological simulation models. Hydrol Sci J. https://doi.org/10.1080/02626668609491024
  • 30. Koren V, Zhang Z, Zhang Y, Reed SM, Cui Z, Moreda F, Cosgrove BA, Mizukami N, Anderson EA (2012) Results of the DMIP 2 Oklahoma experiments. J Hydrol 418–419:17–48. https://doi.org/10.1016/J.JHYDROL.2011.08.056
  • 31. Lewis D, Singer MJ, Tate KW (2000) Applicability of SCS curve number method for a California oak woodlands watershed. J Soil Water Conserv 55:226–230
  • 32. Liu X, Li J (2008) Application of SCS model in estimation of runoff from small watershed in Loess Plateau of China. Chin Geogr Sci 18:235. https://doi.org/10.1007/s11769-008-0235-x
  • 33. Lobligeois F, Andréassian V, Perrin C, Tabary P, Loumagne C (2014) When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events. Hydrol Earth Syst Sci 18:575–594. https://doi.org/10.5194/hess-18-575-2014
  • 34. Ludwig R, Roson R, Zografos C, Kallis G (2011) Towards an inter-disciplinary research agenda on climate change, water and security in Southern Europe and neighboring countries. Environ Sci Policy. https://doi.org/10.1016/j.envsci.2011.04.003
  • 35. Mckee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Eighth conference on applied climatology, pp 179–184
  • 36. Michel C, Andréassian V, Perrin C (2005) Soil conservation service curve number method: How to mend a wrong soil moisture accounting procedure? Water Resour Res. https://doi.org/10.1029/2004wr003191
  • 37. Mishra SK, Singh VP, Sansalone JJ, Aravamuthan V (2003) A Modified SCS-CN Method: characterization and Testing. Water Resour Manag 17:37–68. https://doi.org/10.1023/A:1023099005944
  • 38. Moriasi DN, Arnold JG, Van Liew MW, Bingner R, Harmel R, Veith T (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
  • 39. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
  • 40. Oudin L, Hervieu F, Michel C, Perrin C, Andréassian V, Anctil F, Loumagne C (2005) Which potential evapotranspiration input for a lumped rainfall–runoff model? Part 2—towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling. J Hydrol 303:290–306. https://doi.org/10.1016/J.JHYDROL.2004.08.026
  • 41. Oudin L, Andréassian V, Perrin C, Michel C, Le Moine N (2008) Spatial proximity, physical similarity, regression and ungaged catchments: a comparison of regionalization approaches based on 913 French catchments. Water Resour Res. https://doi.org/10.1029/2007WR006240
  • 42. Pugliese A, Castellarin A, Brath A (2014) Geostatistical prediction of flow-duration curves in an index-flow framework. Hydrol Earth Syst Sci 18:3801–3816. https://doi.org/10.5194/hess-18-3801-2014
  • 43. Roth V, Lemann T (2016) Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands. Hydrol Earth Syst Sci 20:921–934. https://doi.org/10.5194/hess-20-921-2016
  • 44. Saha S, Moorthi S, Pan HL, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Hou Y, Chuang H, Juang H, Sela J, Iredell M, Treadon R, Kleist D, Van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, van den Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm J, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou C, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds R, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1058. https://doi.org/10.1175/2010BAMS3001.1
  • 45. Sangati M, Borga M (2009) Influence of rainfall spatial resolution on flash flood modelling. Nat Hazards Earth Syst Sci 9:575–584
  • 46. Schuol J, Abbaspour KC, Yang H, Srinivasan R, Zehnder A (2008) Modeling blue and green water availability in Africa. Water Resour Res. https://doi.org/10.1029/2007WR006609
  • 47. Sellami H, La Jeunesse I, Benabdallah S, Vanclooster M (2013) Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments. Hydrol Sci J. https://doi.org/10.1080/02626667.2013.837222
  • 48. Sherman J, Morrison WJ (1950) Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. Ann Math Stat 21:124–127
  • 49. Singh J, Knapp HV, Demissie M (2005) Hydrologic modeling of the Iroquois river watershed using HSPF and SWAT. J Am Water Resour Assoc 41(2):343–360. https://doi.org/10.1111/j.1752-1688.2005.tb03740.x
  • 50. Tegegne G, Park DK, Kim Y-O (2017) Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin. J Hydrol Reg Stud 14:49–66. https://doi.org/10.1016/J.EJRH.2017.10.002
  • 51. Tolera M, Chung I-M, Chang S (2018) Evaluation of the climate forecast system reanalysis weather data for watershed modeling in Upper Awash Basin. Ethiopia. Water 10:725. https://doi.org/10.3390/w10060725
  • 52. Tramblay Y, Bouvier C, Ayral P-A, Marchandise A (2011) Impact of rainfall spatial distribution on rainfall–runoff modelling efficiency and initial soil moisture conditions estimation. Nat Hazards Earth Syst Sci 11:157–170. https://doi.org/10.5194/nhess-11-157-2011
  • 53. Vansteenkiste T, Tavakoli M, Van Steenbergen N, De Smedt F, Batelaan O, Pereira F, Willems P (2014) Intercomparison of five lumped and distributed models for catchment runoff and extreme flow simulation. J Hydrol 511:335–349. https://doi.org/10.1016/J.JHYDROL.2014.01.050
  • 54. Wöhling T, Samaniego L, Kumar R (2013) Evaluating multiple performance criteria to calibrate the distributed hydrological model of the upper Neckar catchment. Environ Earth Sci 69:453–468. https://doi.org/10.1007/s12665-013-2306-2
  • 55. Zhang Q, Heiner K, Holmgren K (2013) How well do reanalyses represent the southern African precipitation? Clim Dyn. https://doi.org/10.1007/s00382-012-1423-z
  • 56. Zkhiri W, Tramblay Y, Hanich L et al (2019) Spatiotemporal characterization of current and future droughts in the High Atlas basins (Morocco). Theor Appl Climatol 135:593–605. https://doi.org/10.1007/s00704-018-2388-6
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c4620145-647c-4718-af02-16d40b148b3d
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.