Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5387-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-5387-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Manuel F. Rios Gaona
CORRESPONDING AUTHOR
School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, UK
Katerina Michaelides
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Cabot Institute for the Environment, University of Bristol, Bristol, BS8 1QU, UK
Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
Michael Bliss Singer
School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, UK
Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK
Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
Related authors
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
George Blake, Katerina Michaelides, Elizabeth Kendon, Mark Cuthbert, and Michael Singer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1154, https://doi.org/10.5194/egusphere-2025-1154, 2025
Short summary
Short summary
Dryland rainfall mainly falls during localised storms, with the intensity of these storms key in controlling how water moves through the landscape, but most climate models cannot represent these storms accurately. We find that if you use a model that can represent these storms to understand water resources, you end up with higher soil moisture for plants and groundwater for humans. Any studies of future water resources that don’t use high-resolution models could produce misleading projections.
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024, https://doi.org/10.5194/hess-28-4203-2024, 2024
Short summary
Short summary
Reservoirs are essential for water resource management and can significantly impact downstream flow. However, representing reservoirs in hydrological models can be challenging, particularly across large scales. We design a new and simple method for simulating river flow downstream of water supply reservoirs using only open-access data. We demonstrate the approach in 264 reservoir catchments across Great Britain, where we can significantly improve the simulation of reservoir-impacted flow.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
Short summary
Short summary
Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Shiuan-An Chen, Katerina Michaelides, David A. Richards, and Michael Bliss Singer
Earth Surf. Dynam., 10, 1055–1078, https://doi.org/10.5194/esurf-10-1055-2022, https://doi.org/10.5194/esurf-10-1055-2022, 2022
Short summary
Short summary
Drainage basin erosion rates influence landscape evolution through controlling land surface lowering and sediment flux, but gaps remain in understanding their large-scale patterns and drivers between timescales. We analysed global erosion rates and show that long-term erosion rates are controlled by rainfall, former glacial processes, and basin landform, whilst human activities enhance short-term erosion rates. The results highlight the complex interplay of controls on land surface processes.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, https://doi.org/10.5194/gmd-14-6893-2021, 2021
Short summary
Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Maria Magdalena Warter, Michael Bliss Singer, Mark O. Cuthbert, Dar Roberts, Kelly K. Caylor, Romy Sabathier, and John Stella
Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, https://doi.org/10.5194/hess-25-3713-2021, 2021
Short summary
Short summary
Intensified drying of soil and grassland vegetation is raising the impact of fire severity and extent in Southern California. While browned grassland is a common sight during the dry season, this study has shown that there is a pronounced shift in the timing of senescence, due to changing climate conditions favoring milder winter temperatures and increased precipitation variability. Vegetation may be limited in its ability to adapt to these shifts, as drought periods become more frequent.
Isaac Kipkemoi, Katerina Michaelides, Rafael Rosolem, and Michael Bliss Singer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-48, https://doi.org/10.5194/hess-2021-48, 2021
Manuscript not accepted for further review
Short summary
Short summary
The work is a novel investigation of the role of temporal rainfall resolution and intensity in affecting the water balance of soil in a dryland environment. This research has implications for what rainfall data are used to assess the impact of climate and climate change on the regional water balance. This information is critical for anticipating the impact of a changing climate on dryland communities globally who need it to know when to plant their seeds or where livestock pasture is available.
Cited articles
Anaconda Software Distribution: Conda, https://anaconda.com (last access: 7 July 2024), 2023. a
Asfaw, D. T., Singer, M. B., Rosolem, R., MacLeod, D., Cuthbert, M., Miguitama, E. Q., Rios Gaona, M. F., and Michaelides, K.: stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts, Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, 2023. a
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, 2018. a, b
Berger, D.: Kendall's Rank Correlation vs Pearson's Linear Correlation: A Proof Of Greiner's Relation, SSRN, https://doi.org/10.2139/ssrn.2837712, 2016. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bonan, G. B.: A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide (No. NCAR/TN-417+STR), Tech. Rep. NCAR/TN-417+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6DF6P5X, 1996. a
Branch, M. A., Coleman, T. F., and Li, Y.: A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems, SIAM J. Sci. Comput., 21, 1–23, https://doi.org/10.1137/S1064827595289108, 1999. a
Breitenberger, E.: Analogues of the normal distribution on the circle and the sphere, Biometrika, 50, 81–88, https://doi.org/10.1093/biomet/50.1-2.81, 1963. a
Burton, A., Kilsby, C., Fowler, H., Cowpertwait, P., and O'Connell, P.: RainSim: A spatial-temporal stochastic rainfall modelling system, Environ. Modell. Softw., 23, 1356–1369, https://doi.org/10.1016/j.envsoft.2008.04.003, 2008. a
Caylor, K. K., D'Odorico, P., and Rodriguez-Iturbe, I.: On the ecohydrology of structurally heterogeneous semiarid landscapes, Water Resour. Res., 42, W07424, https://doi.org/10.1029/2005WR004683, 2006. a
Chen, L. and Guo, S.: Copulas and Its Application in Hydrology and Water Resources, no. 2364-8198 in Springer Water, Springer Singapore, 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore, 1st edn., https://doi.org/10.1007/978-981-13-0574-0, 2019. a, b
Cokelaer, T., Kravchenko, A., lahdjirayhan, msat59, Varma, A., L, B., Stringari, C. E., Brueffer, C., Broda, E., Pruesse, E., Singaravelan, K., Li, Z., mark padgham, and negodfre: cokelaer/fitter: v1.6.0, Zenodo [code], https://doi.org/10.5281/zenodo.8226571, 2023. a
Cuthbert, M. O., Acworth, R. I., Andersen, M. S., Larsen, J. R., McCallum, A. M., Rau, G. C., and Tellam, J. H.: Understanding and quantifying focused, indirect groundwater recharge from ephemeral streams using water table fluctuations, Water Resour. Res., 52, 827–840, https://doi.org/10.1002/2015WR017503, 2016. a
Dai, Q., Han, D., Rico-Ramirez, M. A., and Islam, T.: Modelling radar-rainfall estimation uncertainties using elliptical and Archimedean copulas with different marginal distributions, Hydrol. Sci. J., 59, 1992–2008, https://doi.org/10.1080/02626667.2013.865841, 2014. a, b
Dawkins, L. C., Osborne, J. M., Economou, T., Darch, G. J., and Stoner, O. R.: The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator, J. Hydrol., 607, 127478, https://doi.org/10.1016/j.jhydrol.2022.127478, 2022. a, b
Dawson, T. E. and Ehleringer, J. R.: Streamside trees that do not use stream water, Nature, 350, 335–337, https://doi.org/10.1038/350335a0, 1991. a
De Luca, D. L. and Petroselli, A.: STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series, Hydrology, 8, 76, https://doi.org/10.3390/hydrology8020076, 2021. a, b
Dhillon, I. and Sra, S.: Modeling Data using Directional Distributions, Tech. Rep. TR-03-06, University of Texas: Department of Computer Science, Austin, TX, USA, https://www.cs.utexas.edu/users/inderjit/public_papers/tr03-06.pdf (last access: 7 July 2024), 2003. a
Diaz, H. F. and Markgraf, V. (Eds.): El Niño and the Southern Oscillation: Multiscale Variability and Global and Regional Impacts, Cambridge University Press, https://doi.org/10.1017/CBO9780511573125, 2000. a
Diez-Sierra, J., Navas, S., and del Jesus, M.: NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process, Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, 2023. a
D'Odorico, P., Caylor, K., Okin, G. S., and Scanlon, T. M.: On soil moisture-vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems, J. Geophys. Res.-Biogeo., 112, G04010, https://doi.org/10.1029/2006JG000379, 2007. a
Evans, C. M., Dritschel, D. G., and Singer, M. B.: Modeling Subsurface Hydrology in Floodplains, Water Resour. Res., 54, 1428–1459, https://doi.org/10.1002/2017WR020827, 2018. a
Fang, K.-T., Kotz, S., and Ng, K. W.: Symmetric Multivariate and Related Distributions, no. 36 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742, 1st edn., https://doi.org/10.1201/9781351077040, 1990. a
Genest, C., Rémillard, B., and Beaudoin, D.: Goodness-of-fit tests for copulas: A review and a power study, Insurance Math. Econom., 44, 199–213, https://doi.org/10.1016/j.insmatheco.2007.10.005, 2009. a
Goodrich, D. C., Keefer, T. O., Unkrich, C. L., Nichols, M. H., Osborn, H. B., Stone, J. J., and Smith, J. R.: Long-term precipitation database, Walnut Gulch Experimental Watershed, Arizona, United States, Water Resour. Res., 44, W05S04, https://doi.org/10.1029/2006WR005782, 2008. a, b, c
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017. a
Hofert, M., Kojadinovic, I., Mächler, M., and Yan, J.: Elements of Copula Modeling with R, no. 2197-5744 in Use R!, Springer Cham, Gewerbestrasse 11, 6330 Cham, Switzerland, 1st edn., https://doi.org/10.1007/978-3-319-89635-9, 2018. a, b
Hornik, K. and Grün, B.: On maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions, Comput. Statist., 29, 945–957, https://doi.org/10.1007/s00180-013-0471-0, 2013. a
Hornik, K. and Grün, B.: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions, J. Stat. Softw., 58, 1–31, https://doi.org/10.18637/jss.v058.i10, 2014. a
Jammalamadaka, S. R. and SenGupta, A.: Topics in Circular Statistics, no. 5 in Series on Multivariate Analysis, World Scientific, P.O. Box 128, Farrer Road, Singapore 912805, https://doi.org/10.1142/4031, 2001. a, b
Joe, H.: Dependence Modeling with Copulas, no. 134 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742, 1st edn., https://doi.org/10.1201/b17116, 2014. a, b, c
Keefer, T. O., et al.: Southwest Watershed Research Center and Walnut Gulch Experimental Watershed, Tech. Rep. SWRC Publ. Reference No. 1588, Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719, http://www.tucson.ars.ag.gov/unit/publications/PDFfiles/1588.pdf (last access: 7 July 2024), 2007. a, b
Kendall, M. G.: The Treatment of Ties in Ranking Problems, Biometrika, 33, 239–251, https://doi.org/10.1093/biomet/33.3.239, 1945. a
Khedun, C. P., Mishra, A. K., Singh, V. P., and Giardino, J. R.: A copula-based precipitation forecasting model: Investigating the interdecadal modulation of ENSO's impacts on monthly precipitation, Water Resour. Res., 50, 580–600, https://doi.org/10.1002/2013WR013763, 2014. a
Kim, D., Cho, H., Onof, C., and Choi, M.: Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling, Stoch. Env. Res. Risk A., 31, 1023–1043, https://doi.org/10.1007/s00477-016-1234-6, 2017. a, b, c
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. a
Laio, F., D'Odorico, P., and Ridolfi, L.: An analytical model to relate the vertical root distribution to climate and soil properties, Geophys. Res. Lett., 33, L18401, https://doi.org/10.1029/2006GL027331, 2006. a
Langworthy, B. W., Stephens, R. L., Gilmore, J. H., and Fine, J. P.: Canonical correlation analysis for elliptical copulas, J. Multivariate Anal., 183, 104715, https://doi.org/10.1016/j.jmva.2020.104715, 2021. a
Lark, R. M., Clifford, D., and Waters, C. N.: Modelling complex geological circular data with the projected normal distribution and mixtures of von Mises distributions, Solid Earth, 5, 631–639, https://doi.org/10.5194/se-5-631-2014, 2014. a
Mardia, K. and Jupp, P.: Directional Statistics, Wiley Series in Probability and Statistics, John Wiley & Sons, Ltd., West Sussex, PO19 1UD England, https://doi.org/10.1002/9780470316979, 1999. a, b, c
Meles, M. B., Demaria, E. M. C., Heilman, P., Goodrich, D. C., Kautz, M. A., Armendariz, G., Unkrich, C., Wei, H., and Perumal, A. T.: Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets, Water, 14, 2198, https://doi.org/10.3390/w14142198, 2022. a, b, c
Michaelides, K. and Martin, G. J.: Sediment transport by runoff on debris-mantled dryland hillslopes, J. Geophys. Res.-Earth Surf., 117, F03014, https://doi.org/10.1029/2012JF002415, 2012. a
Michaelides, K. and Singer, M. B.: Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems, J. Geophys. Res.-Earth Surf., 119, 1205–1221, https://doi.org/10.1002/2013JF002959, 2014. a
Michaelides, K. and Wainwright, J.: Modelling the effects of hillslope-channel coupling on catchment hydrological response, Earth Surf. Proc. Land., 27, 1441–1457, https://doi.org/10.1002/esp.440, 2002. a
Michaelides, K. and Wilson, M. D.: Uncertainty in predicted runoff due to patterns of spatially variable infiltration, Water Resour. Res., 43, W02415, https://doi.org/10.1029/2006WR005039, 2007. a
Michaelides, K., Hollings, R., Singer, M. B., Nichols, M. H., and Nearing, M. A.: Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin, Earth Surf. Proc. Land., 43, 1608–1621, https://doi.org/10.1002/esp.4340, 2018. a
Moran, M. S., Holifield Collins, C. D., Goodrich, D. C., Qi, J., Shannon, D. T., and Olsson, A.: Long-term remote sensing database, Walnut Gulch Experimental Watershed, Arizona, United States, Water Resour. Res., 44, W05S10, https://doi.org/10.1029/2006WR005689, 2008. a, b
Moré, J. J., Garbow, B. S., and Hillstrom, K. E.: User Guide for MINPACK-1, Tech. Rep. ANL-80-74, Argonne National Laboratory, Argonne, IL, USA, https://www.math.utah.edu/software/minpack/ (last access: 7 July 2024), 1980. a
Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., and Sorooshian, S.: Rainfall modeling for integrating radar information into hydrological model, Atmos. Sci. Lett., 6, 23–30, https://doi.org/10.1002/asl.86, 2005. a, b, c
Nelsen, R. B.: An Introduction to Copulas, no. 2197-568X in Springer Series in Statistics, Springer New York, New York, NY 10013, USA, 2nd edn., https://doi.org/10.1007/0-387-28678-0, 2006. a, b
Nicholson, S. E.: Dryland Climatology, Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK, https://doi.org/10.1017/CBO9780511973840, 2011. a, b
Osborn, H. B.: Timing and duration of high rainfall rates in the southwestern United States, Water Resour. Res., 19, 1036–1042, https://doi.org/10.1029/WR019i004p01036, 1983. a
Osborn, H. B. and Lane, L.: Precipitation-runoff relations for very small semiarid rangeland watersheds, Water Resour. Res., 5, 419–425, https://doi.org/10.1029/WR005i002p00419, 1969. a
Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond, Water Resour. Res., 57, e2020WR029466, https://doi.org/10.1029/2020WR029466, 2021. a
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. a, b
Peleg, N., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Syst., 9, 1595–1627, https://doi.org/10.1002/2016MS000854, 2017. a
Philander, S. G.: El Niño, La Niña, and the Southern Oscillation., no. 46 in International Geophysics Series, Academic Press, San Diego, California 92101, US, 1990. a
Powell, M. J. D.: A Hybrid Method for Nonlinear Equations, in: Numerical Methods for Nonlinear Algebraic Equations, edited by Rabinowitz, P., chap. 6, 87–114, Gordon and Breach Science Publishers, 150 Fifth Avenue, New York, N.Y. 10011, U.S., 1970. a
Powell, M. J. D.: On nonlinear optimization since 1959, in: The Birth of Numerical Analysis, edited by Bulthee, A. and Cools, R., 141–160, World Scientific, 5 Toh Tuck Link, Singapore 596224, https://doi.org/10.1142/9789812836267_0009, 2009. a
Quichimbo, E. A., Singer, M. B., and Cuthbert, M. O.: Characterising groundwater-surface water interactions in idealised ephemeral stream systems, Hydrol. Process., 34, 3792–3806, https://doi.org/10.1002/hyp.13847, 2020. a
Quichimbo, E. A., Singer, M. B., Michaelides, K., Hobley, D. E. J., Rosolem, R., and Cuthbert, M. O.: DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance, Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, 2021. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 7 July 2024), 2023. a
Renard, K. G. and Keppel, R. V.: Hydrographs of Ephemeral Streams in the Southwest, J. Hydr. Eng. Div-asce., 92, 33–52, https://doi.org/10.1061/JYCEAJ.0001419, 1966. a
Renard, K. G. and Laursen, E. M.: Dynamic Behavior Model of Ephemeral Stream, J. Hydr. Eng. Div-asce., 101, 511–528, https://doi.org/10.1061/JYCEAJ.0004340, 1975. a
Rios Gaona, M. F.: feliperiosg/STORM2: v2.2.2, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8071820, 2023. a
Rios Gaona, M. F. and Villarini, G.: Characterization of the diurnal cycle of maximum rainfall in tropical cyclones, J. Hydrol., 564, 997–1007, https://doi.org/10.1016/j.jhydrol.2018.07.062, 2018. a
Ross, S. M.: Simulation, Academic Press, Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands, 5th edn., https://doi.org/10.1016/C2011-0-04574-X, 2013. a
Sabathier, R., Singer, M. B., Stella, J. C., Roberts, D. A., and Caylor, K. K.: Vegetation responses to climatic and geologic controls on water availability in southeastern Arizona, Environ. Res. Lett., 16, 064029, https://doi.org/10.1088/1748-9326/abfe8c, 2021. a
Salvadori, G. and De Michele, C.: Statistical characterization of temporal structure of storms, Adv. Water Resour., 29, 827–842, https://doi.org/10.1016/j.advwatres.2005.07.013, 2006. a
Sarachik, E. S. and Cane, M. A.: The El Niño-Southern Oscillation Phenomenon, Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK, https://doi.org/10.1017/CBO9780511817496, 2010. a
Sargeant, C. I. and Singer, M. B.: Sub-annual variability in historical water source use by Mediterranean riparian trees, Ecohydrology, 9, 1328–1345, https://doi.org/10.1002/eco.1730, 2016. a
Scanlon, B. R., Keese, K. E., Flint, A. L., Flint, L. E., Gaye, C. B., Edmunds, W. M., and Simmers, I.: Global synthesis of groundwater recharge in semiarid and arid regions, Hydrol. Process., 20, 3335–3370, https://doi.org/10.1002/hyp.6335, 2006. a
Seabold, S. and Perktold, J.: Statsmodels: Econometric and Statistical Modeling with Python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., 92–96, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a, b
Shmaliy, Y. S.: Von Mises/Tikhonov-based distributions for systems with differential phase measurement, Signal Process., 85, 693–703, https://doi.org/10.1016/j.sigpro.2004.11.008, 2005. a
Singer, M. B. and Michaelides, K.: How is topographic simplicity maintained in ephemeral dryland channels?, Geology, 42, 1091–1094, https://doi.org/10.1130/G36267.1, 2014. a
Singer, M. B. and Michaelides, K.: Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall, Environ. Res. Lett., 12, 104011, https://doi.org/10.1088/1748-9326/aa8e50, 2017. a
Singer, M. B., Sargeant, C. I., Piégay, H., Riquier, J., Wilson, R. J. S., and Evans, C. M.: Floodplain ecohydrology: Climatic, anthropogenic, and local physical controls on partitioning of water sources to riparian trees, Water Resour. Res., 50, 4490–4513, https://doi.org/10.1002/2014WR015581, 2014. a
Singer, M. B., Michaelides, K., and Hobley, D. E. J.: STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change, Geosci. Model Dev., 11, 3713–3726, https://doi.org/10.5194/gmd-11-3713-2018, 2018. a, b, c
Stillman, S., Zeng, X., Shuttleworth, W. J., Goodrich, D. C., Unkrich, C. L., and Zreda, M.: Spatiotemporal Variability of Summer Precipitation in Southeastern Arizona, J. Hydrometeorol., 14, 1944–1951, https://doi.org/10.1175/JHM-D-13-017.1, 2013. a
Temme, N.: On the numerical evaluation of the modified bessel function of the third kind, J. Comput. Phys., 19, 324–337, https://doi.org/10.1016/0021-9991(75)90082-0, 1975. a
The Economist: In defense of the Gaussian copula, electronic periodical, https://www.economist.com/free-exchange/2009/04/29/in-defense-of-the-gaussian-copula (last access: 5 October 2022), 2009. a
Tjøstheim, D., Otneim, H., and Støve, B.: Statistical Modeling Using Local Gaussian Approximation, Academic Press, 125 London Wall, London EC2Y 5AS, United Kingdom, https://doi.org/10.1016/C2017-0-02646-0, 2022. a, b, c
Tucker, G. E. and Bras, R. L.: A stochastic approach to modeling the role of rainfall variability in drainage basin evolution, Water Resour. Res., 36, 1953–1964, https://doi.org/10.1029/2000WR900065, 2000. a
Tucker, G. E. and Hancock, G. R.: Modelling landscape evolution, Earth Surf. Proc. Land., 35, 28–50, https://doi.org/10.1002/esp.1952, 2010. a
Tucker, G. E. and Slingerland, R.: Drainage basin responses to climate change, Water Resour. Res., 33, 2031–2047, https://doi.org/10.1029/97WR00409, 1997. a
UniData: Network Common Data Form (NetCDF), UniData [software], https://doi.org/10.5065/D6H70CW6, 2023. a
Vandenberghe, S., Verhoest, N. E. C., Onof, C., and De Baets, B.: A comparative copula-based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett-Lewis modeled rainfall, Water Resour. Res., 47, W07529, https://doi.org/10.1029/2009WR008388, 2011. a, b
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace, Scotts Valley, CA, ISBN 978-1-4414-1269-0, 2009. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b, c, d
Vu, T. M., Mishra, A. K., Konapala, G., and Liu, D.: Evaluation of multiple stochastic rainfall generators in diverse climatic regions, Stoch. Env. Res. Risk A., 32, 1337–1353, https://doi.org/10.1007/s00477-017-1458-0, 2018. a, b
Warter, M. M., Singer, M. B., Cuthbert, M. O., Roberts, D., Caylor, K. K., Sabathier, R., and Stella, J.: Drought onset and propagation into soil moisture and grassland vegetation responses during the 2012–2019 major drought in Southern California, Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, 2021. a
Warter, M. M., Singer, M. B., Cuthbert, M. O., Roberts, D., Caylor, K. K., Sabathier, R., and Stella, J.: Modeling seasonal vegetation phenology from hydroclimatic drivers for contrasting plant functional groups within drylands of the Southwestern USA, Environ. Res.-Ecology, 2, 025001, https://doi.org/10.1088/2752-664X/acb9a0, 2023. a
Wheater, H. S., Mathias, S. A., and Li, X.: Groundwater Modelling in Arid and Semi-Arid Areas, Cambridge University Press, https://doi.org/10.1017/CBO9780511760280, 2010. a
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. a
Zhang, L. and Singh, V. P.: Copulas and their Applications in Water Resources Engineering, Cambridge University Press, University Printing House, Cambridge CB2 8BS, United Kingdom, https://doi.org/10.1017/9781108565103, 2019. a, b, c
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly...