[{"page":"627-641","extern":"1","year":"2019","abstract":[{"lang":"eng","text":"Exploring the effects of climate change on the hydrological response at the local scale requires climate data at high spatial and temporal resolutions. This is best achieved by generating downscaled ensembles of future climate variables derived from climate models. For this purpose we present a methodology to re-parameterize the AWE-GEN-2d model (Advanced WEather GENerator for a two-dimensional grid). The model simulates key meteorological variables needed by hydrological models and is particularly suitable to explore the effects of stochastic (natural) climatic uncertainty, which is fundamental for hydrological applications, especially at sub-kilometer and hourly scales. Factors of change for different climate statistics are calculated from climate model simulations of present and future climates and subsequently applied to the statistics derived from observations to re-parameterize AWE-GEN-2d. The model abilities in generating an ensemble of future climate variables for the transient period 2020–2089 is presented with examples of precipitation and near-surface air temperature fields from hourly to multi-annual scales for a small mountainous region in the Swiss Alps. The stochastic uncertainty is examined for present and future periods and for spatial scales from the RCM scale (12-km, daily) to 2-km demonstrating the potential use of AWE-GEN-2d outputs. At the RCM scale, model results yield a small increase in annual precipitation (4%) which is within the stochastic uncertainty range for present and future periods (7%). At the fine scale of 2-km, the increase in annual precipitation can exceed the stochastic uncertainty, but for less than 10% of the domain area. On the contrary, changes in annual near-surface air temperature exceed stochastic uncertainty both at the RCM and finer scales. Stochastic climate uncertainty was concluded to be very similar when comparing present and future periods and 12-km and 2-km scales. The benefits of using AWE-GEN-2d in hydrological climate change impact assessments are finally discussed."}],"keyword":["Weather generator","Stochastic downscaling","Climate change","Internal climate variability","Climate uncertainty","High-resolution rainfall model"],"_id":"22551","OA_type":"closed access","scopus_import":"1","day":"01","article_processing_charge":"No","date_published":"2019-04-01T00:00:00Z","status":"public","citation":{"apa":"Peleg, N., Molnar, P., Burlando, P., &#38; Fatichi, S. (2019). Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. <i>Journal of Hydrology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jhydrol.2019.02.010\">https://doi.org/10.1016/j.jhydrol.2019.02.010</a>","ama":"Peleg N, Molnar P, Burlando P, Fatichi S. Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. <i>Journal of Hydrology</i>. 2019;571:627-641. doi:<a href=\"https://doi.org/10.1016/j.jhydrol.2019.02.010\">10.1016/j.jhydrol.2019.02.010</a>","short":"N. Peleg, P. Molnar, P. Burlando, S. Fatichi, Journal of Hydrology 571 (2019) 627–641.","mla":"Peleg, Nadav, et al. “Exploring Stochastic Climate Uncertainty in Space and Time Using a Gridded Hourly Weather Generator.” <i>Journal of Hydrology</i>, vol. 571, Elsevier, 2019, pp. 627–41, doi:<a href=\"https://doi.org/10.1016/j.jhydrol.2019.02.010\">10.1016/j.jhydrol.2019.02.010</a>.","ista":"Peleg N, Molnar P, Burlando P, Fatichi S. 2019. Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator. Journal of Hydrology. 571, 627–641.","ieee":"N. Peleg, P. Molnar, P. Burlando, and S. Fatichi, “Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator,” <i>Journal of Hydrology</i>, vol. 571. Elsevier, pp. 627–641, 2019.","chicago":"Peleg, Nadav, Peter Molnar, Paolo Burlando, and Simone Fatichi. “Exploring Stochastic Climate Uncertainty in Space and Time Using a Gridded Hourly Weather Generator.” <i>Journal of Hydrology</i>. Elsevier, 2019. <a href=\"https://doi.org/10.1016/j.jhydrol.2019.02.010\">https://doi.org/10.1016/j.jhydrol.2019.02.010</a>."},"publication_status":"published","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","doi":"10.1016/j.jhydrol.2019.02.010","date_created":"2026-07-27T12:30:24Z","publisher":"Elsevier","das_tickbox":"1","author":[{"first_name":"Nadav","last_name":"Peleg","full_name":"Peleg, Nadav"},{"last_name":"Molnar","full_name":"Molnar, Peter","first_name":"Peter"},{"last_name":"Burlando","full_name":"Burlando, Paolo","first_name":"Paolo"},{"first_name":"Simone","id":"cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6","full_name":"Fatichi, Simone","last_name":"Fatichi"}],"intvolume":"       571","article_type":"original","date_updated":"2026-08-06T08:45:41Z","quality_controlled":"1","type":"journal_article","title":"Exploring stochastic climate uncertainty in space and time using a gridded hourly weather generator","language":[{"iso":"eng"}],"publication":"Journal of Hydrology","month":"04","volume":571,"publication_identifier":{"issn":["0022-1694"],"eissn":["1879-2707"]},"oa_version":"None"},{"article_processing_charge":"No","day":"01","keyword":["Weather generator","Stochastic downscaling","Climate change","Hydro-meteorology","Rainfall model"],"scopus_import":"1","OA_type":"closed access","_id":"22556","page":"448-467","extern":"1","abstract":[{"lang":"eng","text":"Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961–2000) and forecasting future (2081–2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario."}],"year":"2011","publisher":"Elsevier","date_created":"2026-07-27T12:30:24Z","doi":"10.1016/j.advwatres.2010.12.013","intvolume":"        34","author":[{"id":"cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6","first_name":"Simone","full_name":"Fatichi, Simone","last_name":"Fatichi"},{"first_name":"Valeriy Y.","last_name":"Ivanov","full_name":"Ivanov, Valeriy Y."},{"full_name":"Caporali, Enrica","last_name":"Caporali","first_name":"Enrica"}],"das_tickbox":"1","publication_status":"published","citation":{"apa":"Fatichi, S., Ivanov, V. Y., &#38; Caporali, E. (2011). Simulation of future climate scenarios with a weather generator. <i>Advances in Water Resources</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.advwatres.2010.12.013\">https://doi.org/10.1016/j.advwatres.2010.12.013</a>","ama":"Fatichi S, Ivanov VY, Caporali E. Simulation of future climate scenarios with a weather generator. <i>Advances in Water Resources</i>. 2011;34(4):448-467. doi:<a href=\"https://doi.org/10.1016/j.advwatres.2010.12.013\">10.1016/j.advwatres.2010.12.013</a>","short":"S. Fatichi, V.Y. Ivanov, E. Caporali, Advances in Water Resources 34 (2011) 448–467.","mla":"Fatichi, Simone, et al. “Simulation of Future Climate Scenarios with a Weather Generator.” <i>Advances in Water Resources</i>, vol. 34, no. 4, Elsevier, 2011, pp. 448–67, doi:<a href=\"https://doi.org/10.1016/j.advwatres.2010.12.013\">10.1016/j.advwatres.2010.12.013</a>.","ista":"Fatichi S, Ivanov VY, Caporali E. 2011. Simulation of future climate scenarios with a weather generator. Advances in Water Resources. 34(4), 448–467.","chicago":"Fatichi, Simone, Valeriy Y. Ivanov, and Enrica Caporali. “Simulation of Future Climate Scenarios with a Weather Generator.” <i>Advances in Water Resources</i>. Elsevier, 2011. <a href=\"https://doi.org/10.1016/j.advwatres.2010.12.013\">https://doi.org/10.1016/j.advwatres.2010.12.013</a>.","ieee":"S. Fatichi, V. Y. Ivanov, and E. Caporali, “Simulation of future climate scenarios with a weather generator,” <i>Advances in Water Resources</i>, vol. 34, no. 4. Elsevier, pp. 448–467, 2011."},"status":"public","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","date_published":"2011-04-01T00:00:00Z","title":"Simulation of future climate scenarios with a weather generator","issue":"4","type":"journal_article","quality_controlled":"1","date_updated":"2026-08-06T10:13:12Z","article_type":"original","oa_version":"None","publication_identifier":{"issn":["0309-1708"]},"month":"04","publication":"Advances in Water Resources","volume":34,"language":[{"iso":"eng"}]}]