Articles | Volume 26, issue 8
https://doi.org/10.5194/hess-26-2113-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2113-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Apr 2022
Research article |
| 27 Apr 2022
| 27 Apr 2022
Stochastic daily rainfall generation on tropical islands with complex topography
Lionel Benoit
CORRESPONDING AUTHOR
Water Resources Research Center, University of Hawai`i at Mānoa, 96822 Honolulu, Hawai`i, USA
GePaSud Laboratory, University of French Polynesia, 98702 Faa'a,
Tahiti, French Polynesia
now at: Biostatistics and Spatial Processes (BioSP), INRAE, 84914
Avignon CEDEX 9, France
Lydie Sichoix
GePaSud Laboratory, University of French Polynesia, 98702 Faa'a,
Tahiti, French Polynesia
Alison D. Nugent
Department of Atmospheric Sciences, School of Ocean and Earth Science and Technology, University of Hawai`i at Mānoa, 96822 Honolulu, Hawai`i, USA
Matthew P. Lucas
Department of Geography, University of Hawai`i at Mānoa, 96822
Honolulu, Hawai`i, USA
Thomas W. Giambelluca
Water Resources Research Center, University of Hawai`i at Mānoa, 96822 Honolulu, Hawai`i, USA
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Cited articles
Ailliot, P., Thompson, C., and Thomson, P.: Space–time modelling of precipitation by using a hidden Markov model and censored Gaussian
distributions, Appl. Stat., 58, 405–426, https://doi.org/10.1111/j.1467-9876.2008.00654.x, 2009.
Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather
generators: an overview of weather type models, Journal de la Société Française de Statistiques, 156, 101–113, 2015.
Akana, C. L. and Gonzalez, K.: Hanau ka Ua Hawaiian Rain Names, Kamehameha Publishing, 327 pp., ISBN 13:9780873362467, 2015.
Allard, D. and Bourotte, M.: Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process, Stoch. Environ. Res. Risk A., 29, 453–462, https://doi.org/10.1007/s00477-014-0913-4, 2015.
Ambrosino, C., Chandler, R. E., and Todd, M. C.: Rainfall-derived growing
season characteristics for agricultural impact assessments in South Africa,
Theor. Appl. Climatol., 115, 411–426, https://doi.org/10.1007/s00704-013-0896-y, 2014.
Bárdossy, A. and Plate, E. J.: Modelling daily rainfall using a semi-Markov representation of circulation pattern occurence, J. Hydrol., 122, 33–47, https://doi.org/10.1016/0022-1694(91)90170-M, 1991.
Bárdossy, A. and Pegram, G. G. S.: Copula based multisite model for
daily precipitation simulation, Hydrol. Earth Syst. Sci., 13, 2299–2314, https://doi.org/10.5194/hess-13-2299-2009, 2009.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Baxevani, A. and Lennartsson, J.: A spatiotemporal precipitation generator
based on a censored latent Gaussian field, Water Resour. Res., 51, 4338–4358, https://doi.org/10.1002/2014WR016455, 2015.
Bennett, B., Thyer, M., Leonard, M., Lambert, M., and Bates, B. C.: A
comprehensive and systematic evaluation framework for a parsimonious daily
rainfall field model, J. Hydrol., 556, 1123–1138,
https://doi.org/10.1016/j.jhydrol.2016.12.043, 2018.
Benoit, L.: StochasticRainfallGenerator_TropicalIslands: Release v1 (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.6462982, 2022.
Benoit, L., Allard, D., and Mariethoz, G.: Stochastic Rainfall Modeling at
Sub-kilometer Scale, Water Resour. Res., 54, 4108–4130, https://doi.org/10.1029/2018WR022817, 2018a.
Benoit, L., Vrac, M., and Mariethoz, G.: Dealing with non-stationarity in
sub-daily stochastic rainfall models, Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, 2018b.
Benoit, L., Vrac, M., and Mariethoz, G.: Nonstationary stochastic rain type generation: accounting for climate drivers, Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, 2020.
Benoit, L., Lucas, M. P., Tseng, H., Huang, Y.-F., Tsang, Y.-P., Nugent, A.
D., Giambelluca, T. W., and Mariethoz, G.: High Space-Time Resolution Observation of Extreme Orographic Rain Gradients in a Pacific Island Catchment, Front. Earth Sci., 8, 546246, https://doi.org/10.3389/feart.2020.546246, 2021.
Breinl, K., Di Baldassarre, G., Girons Lopez, M., Hagenlocher, M., Vico, G.,
and Rutgersson, A.: Can weather generation capture precipitation patterns
across different climates, spatial scales and under data scarcity?, Scient. Rep., 7, 5449, https://doi.org/10.1038/s41598-017-05822-y, 2017.
Brown, J. R., Lengaigne, M., Lintner, B. R., Widlansky, M. J., van der Wiel,
K., Dutheil, C., Linsley, B. K., Matthew, A. J., and Renwick, J.: South Pacific Convergence Zone dynamics, variability and impacts in a changing
climate, Nat. Rev. Earth Environ., 1, 530–543, https://doi.org/10.1038/s43017-020-0078-2, 2020.
Cappelaere, B., Feurer, D., Vischel, T., Ottlé, C., Issoufou, H. B.-A., Saux-Picart, S., Maïnassara, I., Oï, M., Chazarin, J.-P., Barral, H., Coudert, B., and Demarty, J.: Modeling Land Surface Fluxes from Uncertain Rainfall: A Case Study in the Sahel with Field-Driven Stochastic Rainfields, Atmosphere, 11, 465, https://doi.org/10.3390/atmos11050465, 2020.
Caruso, S. J. and Businger, S.: Subtropical Cyclogenesis over the Central North Pacific, Weather Forecast., 21, 193–205, https://doi.org/10.1175/WAF914.1, 2006.
Caseri, A., Javelle, P., Ramos, M. H., and Leblois, E.: Generating precipitation ensembles for flood alert and risk management, J. Flood Risk Manage., 9, 402–415, https://doi.org/10.1111/jfr3.12203, 2016.
Chandler, R. E.: Multisite, multivariate weather generation based on generalized linear models, Environ. Model. Softw., 134, 104867, https://doi.org/10.1016/j.envsoft.2020.104867, 2020.
Daly, C., Slater, M. E., Roberti, J. A., Laseter, S. H., and Swift L. W.:
High-resolution precipitation mapping in a mountainous watershed: ground
truth for evaluating uncertainty in a national precipitation dataset, Int. J. Climatol., 37, 124–137, https://doi.org/10.1002/joc.4986, 2017.
Elison Timm, O., Giambelluca, T. W., and Diaz, H. F.: Statistical downscaling of rainfall changes in Hawai`i based on the CMIP5 global model projections, J. Geophys. Res.-Atmos., 120, 92–112, https://doi.org/10.1002/2014JD022059, 2014.
Foresti, L. and Pozdnoukhov, A.: Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques, Meteorol. Appl., 19, 407–419, https://doi.org/10.1002/met.272, 2012.
Fraley, C. and Raftery, A. E.: Model-Based Clustering, Discriminant Analysis, and Density Estimation, J. Am. Stat. Assoc., 97, 611–631, https://doi.org/10.1198/016214502760047131, 2002.
Frazier, A. G., Elison Timm, O., Giambelluca, T. W., and Diaz, H. F.: The
influence of ENSO, PDO and PNA on secular rainfall variations in Hawai`i,
Clim. Dynam., 51, 2127–2140, https://doi.org/10.1007/s00382-017-4003-4, 2018.
Gabellani, S., Boni, G., Ferraris, L., Von Hardenberg, J., and Provenzale, A.: Propagation of uncertainty from rainfall to runoff: A case study with a
stochastic rainfall generator, Adv. Water Resour., 30, 2061–2071,
https://doi.org/10.1016/j.advwatres.2006.11.015, 2007.
Gangopadhyay, S. and Clark, M.: Statistical downscaling using K-nearest neighbors, Water Resour. Res., 41, W02024, https://doi.org/10.1029/2004WR003444, 2005.
Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y.-L., Chu, P.-S., Eischeid, J. K., and Delparte, D. M.: Online Rainfall Atlas of Hawai`i, B. Am. Meteorol. Soc., 94, 313–316, https://doi.org/10.1175/BAMS-D-11-00228.1, 2013.
Greene, A. M., Robertson, A. W., Smyth, P., and Triglia, S.: Downscaling
projections of Indian monsoon rainfall using a non-homogeneous hidden Markov
model, Q. J. Roy. Meteorol. Soc., 137, 347–359, https://doi.org/10.1002/qj.788, 2011.
HCDP – Hawai`i Climate Data Portal: https://www.hawaii.edu/climate-data-portal/, last access: 15 April 2022.
Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Alonso-Balmaseda, M., Balsamo, G., Bechtold, P., Berrisford, P., Bidlot, J.-R., de Boisséson, E., Bonavita, M., Browne, P., Buizza, R., Dahlgren, P., Dee, D., Dragani, R., Diamantakis, M., Flemming, J., Forbes, R., Geer, A. J., Haiden, T., Hólm, E., Haimberger, L., Hogan, R., Horányi, A., Janiskova, M., Laloyaux, P., Lopez, P., Munoz-Sabater, J., Peubey, C., Radu, R., Richardson, D., Thépaut, J.-N., Vitart, F.,
Yang, X., Zsótér, E., and Zuo, H.: Operational global reanalysis: progress, future directions and synergies with NWP, ERA Rep. Ser. 27, ECMWF, 1–63, https://doi.org/10.21957/tkic6g3wm, 2018.
Hopuare, M., Pontaud, M., Céron, J.-P., Ortega, P., and Laurent, V.:
Climate change, Pacific climate drivers and observed precipitation variability in Tahiti, French Polynesia, Clim. Res., 63, 157–170,
https://doi.org/10.3354/cr01288, 2015.
Hopuare, M., Guglielmino, M., and Ortega, P.: Interactions between intraseasonal and diurnal variability of precipitation in the South Central
Pacific: The case of a small high island, Tahiti, French Polynesia, Int. J. Climatol., 39, 670–686, https://doi.org/10.1002/joc.5834, 2018.
Houze, R. A.: Orographic effects on precipitating clouds, Rev. Geophys., 50, RG000365, https://doi.org/10.1029/2011RG000365, 2012.
Huang, S. P., Quek, S. T., and Phoon, K. K.: Convergence study of the truncated Karhunen–Loeve expansion for simulation of stochastic processes,
Int. J. Numer. Meth. Eng., 52, 1029–1043, https://doi.org/10.1002/nme.255, 2001.
Hughes, J. P. and Guttorp, P.: A non-homogeneous hidden Markov model for
precipitation occurence, Appl. Stat., 48, 15–30, 1999.
Jha, S. K., Mariethoz, G., Evans, J., McCabe, M. F., and Sharma, A.: A space
and time scale-dependent nonlinear geostatistical approach for downscaling
daily precipitation and temperature, Water Resour. Res., 51, 6244–6261, https://doi.org/10.1002/2014WR016729, 2014.
Kleiber, W., Katz, R. W., and Rajagopalan, B.: Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes,
Water Resour. Res., 48, W01523, https://doi.org/10.1029/2011WR011105, 2012.
Krajewski, W. F., Ciach, G., and Habib, E.: An analysis of small-scale
rainfall variability in different climatic regimes, Hydrolog. Sci. J., 48, 151–162, https://doi.org/10.1623/hysj.48.2.151.44694, 2003.
Lantuéjoul, C.: Geostatistical Simulation: Models and Algorithms, Springer Science & Business Media, ISBN 978-3-662-04808-5, 2002.
Laurent, V., Maamaatuaiahutapu, K., Brodien, I., Lombardo, S., Tardy, M., and
Varney, P. : Atlas climatologique de la Polynésie française, Délégation Interrégionale de Polynésie Française, Météo France, ISBN 9782111551916, 232 pp., 2019.
Leblois, E. and Creutin, J. D.: Space-time simulation of intermittent rainfall with prescribed advection field: Adaptation of the turning band
method, Water Resour. Res., 49, 3375–3387, https://doi.org/10.1002/wrcr.20190, 2013.
Longman, R. J., Diaz, H. F., and Giambelluca, T. W.: Sustained Increases in
Lower-Tropospheric Subsidence over the Central Tropical North Pacific Drive a Decline in High-Elevation Rainfall in Hawaii, J. Climate, 28, 8743–8759, https://doi.org/10.1175/JCLI-D-15-0006.1, 2015.
Longman, R. J., Giambelluca, T. W., Nullet, M. A., Frazier, A. G., Kodoma, K., Crausbay, S. D., Krushelnycky, P. D., Cordell, S., Clark, M. P., Newman, A. J., and Jeffrey, R. A.: Compilation of climate data from heterogeneous networks across the Hawaiian Islands, Scient. Data, 5, 180012, https://doi.org/10.1038/sdata.2018.12, 2018.
Longman, R. J., Elison Timm, O., Giambelluca, T. W., and Kaiser, L.: A 20-Year Analysis of Disturbance-Driven Rainfall on O`ahu, Hawai`i, Mon. Weather Rev., 6, 1767–1783, https://doi.org/10.1175/MWR-D-20-0287.1, 2021.
Lyons, S. W.: Empirical Orthogonal Function Analysis of Hawaiian Rainfall, J. Appl. Meteorol., 21, 1713–1729, https://doi.org/10.1175/1520-0450(1982)021<1713:EOFAOH>2.0.CO;2, 1982.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Marra, F. and Morin, E.: Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates, Atmos. Res., 200, 126–138, https://doi.org/10.1016/j.atmosres.2017.09.020, 2018.
Mavromatis, T. and Hansen, J. W.: Interannual variability characteristics
and simulated crop response of four stochastic weather generators, Agr. Forest Meteorol., 109, 283–296, https://doi.org/10.1016/S0168-1923(01)00272-6, 2001.
Mezghani, A. and Hingray, B.: A combined downscaling-disaggregation weather
generator for stochastic generation of multisite hourly weather variables over complex terrain: Development and multi-scale validation for the Upper Rhone River basin, J. Hydrol., 377, 245–260, https://doi.org/10.1016/j.jhydrol.2009.08.033, 2009.
Morin, E., Ryb, T., Gavrieli, I., and Enzel, Y.: Mean, variance, and trends of Levant precipitation over the past 4500 years from reconstructed Dead Sea
levels and stochastic modeling, Quatern. Res., 91, 751–767, https://doi.org/10.1017/qua.2018.98, 2019.
Nerini, D., Besic, N., Sideris, I. V., Germann, U., and Foresti, L.: A
non-stationary stochastic ensemble generator for radar rainfall fields based
on the short-space Fourier transform, Hydrol. Earth Syst. Sci., 21, 2777—2797, https://doi.org/10.5194/hess-21-2777-2017, 2017.
Niemi, T. J., Guillaume, J. H. A., Kokkonen, T., Hoang, T. M. T., and Seed, A. W.: Role of spatial anisotropy in design storm generation: Experiment and
interpretation, Water Resour. Res., 52, 69–89, https://doi.org/10.1002/2015WR017521, 2016.
Opitz, T., Allard, D., and Mariethoz, G.: Semi-parametric resampling with
extremes, Spatial Stat., 42, 100445, https://doi.org/10.1002/2015WR017521, 2021.
Oriani, F., Stisen, S., Demirel, M. C., and Mariethoz, G.: Missing Data
Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types, J. Hydrometeorol., 21, 2325–2341, https://doi.org/10.1175/JHM-D-19-0220.1, 2020.
Papalexiou, S. M. and Serinaldi, F.: Random Fields Simplified: Preserving
Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity, Water Resour. Res., 56, e2019WR026331,
https://doi.org/10.1029/2019WR026331, 2020.
Paschalis, A., Molnar, P., Fatichi, S., and Burlando, P.: A stochastic model
for high-resolution space-time precipitation simulation, Water Resour. Res., 49, 8400–8417, https://doi.org/10.1002/2013WR014437, 2013.
Paschalis, A., Fatichi, S., Molnar, P. M., Rimkus, S., and Burlando, P.: On
the effects of small scale space–time variability of rainfall on basin flood response, J. Hydrol., 514, 313–327, https://doi.org/10.1016/j.advwatres.2013.11.006, 2014.
Paulhus, J. L. and Kohler, M. A.: Interpolation of missing precipitation
records, Mon. Weather Rev., 80, 129–133,
https://doi.org/10.1175/1520-0493(1952)080<0129:IOMPR>2.0.CO;2, 1952.
Peleg, N., Skinner, C., Fatichi, S., and Molnar, P.: Temperature effects on
the spatial structure of heavy rainfall modify catchment hydro-morphological
response, Earth Surf. Dynam., 8, 17–36, https://doi.org/10.5194/esurf-8-17-2020, 2020.
Réchou, A., Flores, O., Jumaux, G., Duflot, V., Bousquet, O., Pouppeville, C., and Bonnardot, F.: Spatio-temporal variability of rainfall
in a high tropical island: Patterns and large-scale drivers in Réunion
Island, Q. J. Roy. Meteorol. Soc., 145, 893–909, https://doi.org/10.1002/qj.3485, 2019.
Richardson, C. W.: Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resour. Res., 17, 182–190, https://doi.org/10.1029/WR017i001p00182, 1981.
Rüschendorf, L.: On the distributional transform, Sklar's theorem, and the empirical copula process, J. Stat. Plan. Infer., 139, 3921–3927, https://doi.org/10.1016/j.jspi.2009.05.030, 2009.
Sanfilippo, K. M.: Predictor selection and model evaluation for future
rainfall projection in Hawai`i, University of Hawai`i at Mānoa,
Honolulu, 124 pp., http://hdl.handle.net/10125/73340 (last access: 15 April 2022), 2020.
Schleiss, M., Chamon, S. and Berne, A.: Nonstationarity in Intermittent Rainfall: The “Dry Drift”, J. Hydrometeorol., 15, 1189–1204,
https://doi.org/10.1175/JHM-D-13-095.1, 2014.
Schwartz, G.: Estimating the dimension of a model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978.
Scott, D. W.: On optimal and data-based histograms, Biometrika, 66, 605–610, https://doi.org/10.1093/biomet/66.3.605, 1979.
Scott, D. W.: Scott's rule, WIREs Comput. Stat., 2, 497–502, https://doi.org/10.1002/wics.103, 2010.
Supit, I., van Diepen, C. A., de Wit, A. J. W., Wolf, J., Kabat, P., Baruth,
B., and Ludwig, F.: Assessing climate change effects on European crop yields
using the Crop Growth Monitoring System and a weather generator, Agr. Forest Meteorol., 164, 96–111, https://doi.org/10.1016/j.agrformet.2012.05.005, 2012.
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical
bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017.
Vrac, M., Stein, M., and Hayhoe, K.: Statistical downscaling of precipitation
through nonhomogeneous stochastic weather typing, Clim. Res., 34, 169–184, https://doi.org/10.3354/cr00696, 2007.
Vu, T. M., Mishra, A. K., Konapala, G., and Liu, D.: Evaluation of multiple
stochastic rainfall generators in diverse climatic regions, Stoch. Environ. Res. Risk A., 32, 1337–1353, https://doi.org/10.1007/s00477-017-1458-0, 2018.
Wilby, R. L.: Stochastic weather type simulation for regional cliamte change
assessment, Water Resour. Res., 30, 3395–3403, https://doi.org/10.1029/94WR01840, 1994.
Wilcox, C., Aly, C., Vischel, T., Panthou, G., Blanchet, J., Quantin, G., and
Lebel, T.: Stochastorm: A Stochastic Rainfall Simulator for Convective Storms, J. Hydrometeorol., 22, 387–404, https://doi.org/10.1175/JHM-D-20-0017.1, 2021.
Wilks, D. S. and Wilby, R. L.: The weather generation game: a review of
stochastic weather models, Prog. Phys. Geogr., 23, 329–357,
https://doi.org/10.1177/030913339902300302, 1999.
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric
circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014.
Short summary
This study presents a probabilistic model able to reproduce the spatial patterns of rainfall on tropical islands with complex topography. It sheds new light on rainfall variability at the island scale, and explores the links between rainfall patterns and atmospheric circulation. The proposed model has been tested on two islands of the tropical Pacific, and demonstrates good skills in simulating both site-specific and island-scale rain behavior.
This study presents a probabilistic model able to reproduce the spatial patterns of rainfall on...
