Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

Qiang Ma , Siyuan Chang , Guowei Lu , Philippe Gourbesville

River ›› 2024, Vol. 3 ›› Issue (4) : 362 -372.

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River ›› 2024, Vol. 3 ›› Issue (4) : 362 -372. DOI: 10.1002/rvr2.106
RESEARCH ARTICLE

Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

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Abstract

To have effective water resource management, the distributed hydrological models are commonly applied for supporting the decision-making processes. Among different inputs, the spatial distributed rainfall plays significant role in those model simulations. Many interpolation methods have been developed for generating distributed rainfall based on measurement samples. However, depending on the catchment characteristics and data availability, the suitable interpolation algorithm is case-dependent. This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment (Var catchment, France). Based on the daily rainfall data (2008–2014) collected from 16 stations in the Var catchment, six different interpolation approaches including: inverse distance weight (IDW), spline, kriging with linear and spherical semi-variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested. Integrated the results of statistical and modeling assessments, the 400m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment. Moreover, the strategy described in the article also shows promising acceptability for other catchments.

Keywords

daily rainfall interpolation / inverse distance weight / rainfall spatial distribution / Var catchment

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Qiang Ma, Siyuan Chang, Guowei Lu, Philippe Gourbesville. Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France. River, 2024, 3(4): 362-372 DOI:10.1002/rvr2.106

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References

[1]

Allamano, P., Claps, P., Laio, F., & Thea, C. (2009). A data-based assessment of the dependence of short-duration precipitation on elevation. Physics and Chemistry of the Earth, Parts A/B/C, 34(10), 635-641.

[2]

Bárdossy, A., & Das, T. (2008). Influence of rainfall observation network on model calibration and application. Hydrology and Earth System Sciences, 12(1), 77-89.

[3]

Basist, A., Bell, G. D., & Meentemeyer, V. (1994). Statistical relationships between topography and precipitation patterns. Journal of Climate, 7(9), 1305-1315.

[4]

Basistha, A., Arya, D. S., & Goel, N. K. (2008). Spatial distribution of rainfall in Indian Himalayas-A case study of Uttarakhand region. Water Resources Management, 22, 1325-1346.

[5]

Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P., Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B. D., & Andreassian, V. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40, 1-20.

[6]

Beven, K. J. (2011). Rainfall-runoff modelling: The primer. John Wiley & Sons.

[7]

Boer, E. P. J., de Beurs, K. M., & Hartkamp, A. D. (2001). Kriging and thin plate splines for mapping climate variables. International Journal of Applied Earth Observation and Geoinformation, 3, 146-154.

[8]

Brunsdon, C., McClatchey, J., & Unwin, D. J. (2001). Spatial variations in the average rainfall-altitude relationship in Great Britain: An approach using geographically weighted regression. International Journal of Climatology, 21(4), 455-466.

[9]

Burrough, P. A., & McDonnell, R. (1998). Principles of geographical information systems. Oxford University Press.

[10]

Buytaert, W., Celleri, R., Willems, P., Bièvre, B. D., & Wyseure, G. (2006). Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. Journal of Hydrology, 329, 413-421.

[11]

Carrera-Hernández, J. J., & Gaskin, S. J. (2007). Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. Journal of Hydrology, 336, 231-249.

[12]

Chen, T., Ren, L., Yuan, F., Yang, X., Jiang, S., Tang, T., Liu, Y., Zhao, C., & Zhang, L. (2017). Comparison of spatial interpolation schemes for rainfall data and application in hydrological modeling. Water, 9, 342.

[13]

Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied hydrology. McGraw-Hill, Inc.

[14]

Deutsch, C. V. (1996). Correcting for negative weights in ordinary kriging. Computers & Geosciences, 22, 765-773.

[15]

Dirks, K., Hay, J., Stow, C., & Harris, D. (1998). High-resolution studies of rainfall on Norfolk Island: Part II: Interpolation of rainfall data. Journal of Hydrology, 208(3e4), 187e193.

[16]

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons.

[17]

Fraga, I., Cea, L., & Puertas, J. (2018). Effect of rainfall uncertainty on the performance of physically based rainfall-runoff models. Hydrological Processes, 2018, 1-14.

[18]

Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press.

[19]

Goovaerts, P. (1998). Ordinary cokriging revisited. Mathematical Geology, 30(1), 21-42.

[20]

Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113-129.

[21]

Gourbesville, P., Du, M., Zavattero, E., Ma, Q., & Gaetano, M. (2018). Decision support system architecture for real time water management. Advances in Hydroinformatics, SimHydro 2017 - Choosing the Right Model in Applied Hydraulics, 259-272.

[22]

Gouvas, M., Sakellariou, N., & Xystrakis, F. (2009). The relationship between altitude of meteorological stations and average monthly and annual precipitation. Studia Geophysica et Geodaetica, 53(4), 557-570.

[23]

Groisman, P. Y., & Legates, D. R. (1994). The accuracy of United States precipitation data. Bulletin of the American Meteorological Society, 75(2), 215-227.

[24]

Hevesi, J. A., Istok, J. D., & Flint, A. L. (1992). Precipitation estimation in mountainous terrain using multivariate geostatistic. Part I: Structural analysis. Journal of Applied Meteorology, 31, 661-676.

[25]

Huard, D., & Mailhot, A. (2006). A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model “abc”. Water Resources Research, 42(7), 1-14.

[26]

Hutchinson, M. F. (1995). Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems, 9(4), 385-403.

[27]

Johansson, B., & Chen, D. (2003). The influence of wind and topography on precipitation distribution in Sweden: Statistical analysis and modelling. International Journal of Climatology, 23(12), 1523-1535.

[28]

Johnson, G. L., & Hanson, C. L. (1995). Topographic and atmospheric influences on precipitation variability over a mountainous watershed. Journal of Applied Meteorology, 34(1), 68-87.

[29]

Kim, C., & Kim, D. H. (2018). Effect of rainfall spatial distribution and duration on minimum spatial resolution of rainfall data for accurate surface runoff prediction. Journal of Hydro-environment Research, 20, 1-8.

[30]

Ly, S., Charles, C., & Degré A. (2011). Geostatistical interpolation of daily rainfall at catchment scale: The use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15, 2259-2274.

[31]

Ly, S., Charles, C., & Degré A. (2013). Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modelling at watershed scale. A review. Biotechnology, Agronomy, Society and Environment 17(2), 392-406.

[32]

Ma, Q., Abily, M., Du, M., Gourbesville, P., & Fouché O. (2020). Integrated groundwater resources management: Spatially-nested modelling approach for water cycle simulation. Water Resources Management, 34, 1319-1333.

[33]

Mair, A., & Fares, A. (2011). Comparison of rainfall interpolation methods in a mountainous region of a tropical island. Journal of Hydrologic Engineering, 16(4), 371-383.

[34]

Nalder, I. A., & Wein, R. W. (1998). Spatial interpolation of climatic normals: Test of a new method in the Canadian boreal forest. Agricultural and Forest Meteorology, 92, 211-225.

[35]

Newlands, N. K., Davidson, A., Howard, A., & Hill, H. (2010). Validation and intercomparison of three methodologies for interpolating daily precipitation and temperature across Canada. Environmetrics (London, Ont.), 22, 205e223.

[36]

Oke, A., Frost, A., & Beesley, C. (2009). The use of TRMM satellite data as a predictor in the spatial interpolation of daily precipitation over Australia. In: Proceedings of the 18th World IMACS/MODSIM Congress.

[37]

Price, D. T., McKenney, D. W., Nalder, I. A., Hutchinson, M. F., & Kesteven, J. L. (2000). A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology, 101(2e3), 81-94.

[38]

Robson, B. J. (2014). State of the art in modelling of phosphorus in aquatic systems: Review, criticisms and commentary. Environmental Modelling & Software, 61, 339-359.

[39]

Sevruk, B. (1997). Regional dependency of precipitation-altitude relationship in the Swiss Alps. Climatic Change, 36(3-4), 355-369.

[40]

Sinclair, M. R., Wratt, D. S., Henderson, R. D., & Gray, W. R. (1997). Factors affecting the distribution and spillover of precipitation in the Southern Alps of New Zealand-A case study. Journal of Applied Meteorology, 36(5), 428-442.

[41]

Singh, V. P. (1998). Effect of the direction of storm movement on planar flow. Hydrological Processes, 12, 147-170.

[42]

Tao, T., Chocat, B., Liu, S., & Xin, K. (2009). Uncertainty analysis of interpolation methods in rainfall spatial distribution-A case of small catchment in Lyon. Journal of Water Resource and Protection, 1, 136-144.

[43]

Vicente Serrano, S., Sanchez, M., Saz-Sánchez, S., & Cuadrat, J. (2003). Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): Application to annual precipitation and temperature. Climate Research, 24, 161-180.

[44]

Vo, N. D., & Gourbesville, P. (2014). Rainfall uncertainty in distributed hydrological modelling in large catchments: An operational approach applied to the Vu Gia-Thu Bon catchment Vietnam. 3rd IAHR Europe Congress, Book of Proceedings, 2014, Porto-Portugal.

[45]

Zavattero, E., Ma, Q., Du, M., & Gourbesville, P. (2016). Construction d’un outil de simulation global des ecoulements superficiels et souterrains dans la basse vallee du var, Rapport d’Avancement - Phase 3.

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2024 The Author(s). River published by Wiley-VCH GmbH on behalf of China Institute of Water Resources and Hydropower Research (IWHR).

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