Relevant bibliographies by topics / Stochastic rainfall model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stochastic rainfall model'

Author: Grafiati
Published: 11 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Stochastic rainfall model"

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Perdana, Damar Adi, Ahmad Zakaria, and Sumiharni Sumiharni. "Studi Pemodelan Curah hujan sintetik dari beberapa stasiun di wilayah Pringsewu." Jurnal Rekayasa Sipil dan Desain 3, no. 1 (2015): 45–56. https://doi.org/10.23960/jrsdd.v3i1.386.

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This research conducted to study the characteristics of daily rainfall and model making ofsynthetic daily rainfall in Pringsewu regency using periodic model, stochastic model and periodicstochastic models. This research conducted using daily rainfall data with length of 1984-2013from three rainfall stations, Pringsewu, Wonokriyo and Banyuwangi rainfall stations.These models performed by using 512 days annual data. Using rainfall frequency obtained andapplying the spectral method and the least squares method, it can be generated the daily rainfallperiodic models. Rainfall stochastic model assum
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Arizona, Bramesvara, Ahmad Zakaria, and Ofik Toupik Purwadi. "Studi Pemodelan Stokastik Curah Hujan Harian di Stasiun Kota Metro." Jurnal Rekayasa Sipil dan Desain 3, no. 1 (2015): 37–44. https://doi.org/10.23960/jrsdd.v3i1.385.

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The purpose of this research is to study the daily rainfall data series The data used daily rainfall data with data length in 1986-2013 at 3 stations namely Metro station R-206, R-107 Raman Dam, and Argoguruh R-106 are located in Metro City and the surrounding areas.The modeling is done using the data length of 512 days. By using the frequency of rainfall data obtained then apply the Fourier equation and the method of least squares is then generated model of periodic daily rainfall. Rainfall stochastic model of rainfall data is assumed as the difference between precipitation data with periodic
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Rebora, Nicola, Luca Ferraris, Jost von Hardenberg, and Antonello Provenzale. "RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model." Journal of Hydrometeorology 7, no. 4 (2006): 724–38. http://dx.doi.org/10.1175/jhm517.1.

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Abstract A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical
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Calder, Ian R. "A stochastic model of rainfall interception." Journal of Hydrology 89, no. 1-2 (1986): 65–71. http://dx.doi.org/10.1016/0022-1694(86)90143-5.

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Keim, R. F., A. E. Skaugset, T. E. Link, and A. Iroumé. "A stochastic model of throughfall for extreme events." Hydrology and Earth System Sciences 8, no. 1 (2004): 23–34. http://dx.doi.org/10.5194/hess-8-23-2004.

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Abstract. Although it is well known that forest canopies reduce the amount and intensity of precipitation at the ground surface, little is known about how canopy interception modifies extreme events. The effects of forest cover on intensity-duration-frequency relationships were investigated, using a stochastic model to extrapolate measured rainfall and throughfall to throughfall expected during extreme events. The model coupled a stochastic model of rainfall with stochastic representations of evaporation and precipitation transfer through canopies. Stochastic evaporation was governed by probab
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Bartlett, M. S., E. Daly, J. J. McDonnell, A. J. Parolari, and A. Porporato. "Stochastic rainfall-runoff model with explicit soil moisture dynamics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2183 (2015): 20150389. http://dx.doi.org/10.1098/rspa.2015.0389.

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Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rai
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D’Onofrio, D., E. Palazzi, J. von Hardenberg, A. Provenzale, and S. Calmanti. "Stochastic Rainfall Downscaling of Climate Models." Journal of Hydrometeorology 15, no. 2 (2014): 830–43. http://dx.doi.org/10.1175/jhm-d-13-096.1.

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Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-A
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R. SUBBAIAH and D. D. SAHU. "Stochastic model for weekly rainfall of Junagadh." Journal of Agrometeorology 4, no. 1 (2002): 65–73. http://dx.doi.org/10.54386/jam.v4i1.427.

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Woolhiser, D. A., and H. B. Osborn. "A Stochastic Model of Dimensionless Thunderstorm Rainfall." Water Resources Research 21, no. 4 (1985): 511–22. http://dx.doi.org/10.1029/wr021i004p00511.

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Misra, A. K., and Amita Tripathi. "Stochastic stability of aerosols-stimulated rainfall model." Physica A: Statistical Mechanics and its Applications 527 (August 2019): 121337. http://dx.doi.org/10.1016/j.physa.2019.121337.

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Dissertations / Theses on the topic "Stochastic rainfall model"

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To, Chun-hung. "Stochastic model of daily rainfall." Click to view the E-thesis via HKUTO, 1989. http://sunzi.lib.hku.hk/hkuto/record/B31976098.

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To, Chun-hung, and 杜振雄. "Stochastic model of daily rainfall." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31976098.

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Alajarmeh, Ramiaah Mohammad Saleh. "Generation of daily rainfall time series using a hybrid stochastic model." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675943.

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Rainfall occurrence and intensity are the most important drivers of the surface runoff process. The knowledge of the occurrence and intensity of rainfall events is a crucial concern for water resources planners and designers. Stochastic rainfall generators are considered a robust tool that can generate the rainfall intensity of any time length at the interested locations. The validity of the stochastic daily rainfall generators for the Middle East in general and Jordan in specific has not been well researched. The aim of the present research is to estimate daily rainfall time series in differe
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Hussain, Arshad. "Stochastic modeling of rainfall processes: a Markov chain - mixed exponential model for rainfalls in different climatic conditions." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18710.

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Watershed models simulating the physical process of runoff usually require daily or sub-daily rainfall time series data as input. However, even when rainfall records are available, they contain only limited and finite information regarding the historical rainfall pattern to adequately assess the response and reliability of a water resource system. This study is therefore concerned with the development of a stochastic rainfall model that can reliably generate many sequences of synthetic rainfall time series' that have similar properties to those of the observed data. The 'MCME' model develop
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Zhang, Xiaohui. "Integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/282409.

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This research presents an integration of a stochastic space-time rainfall model and distributed hydrologic simulation with GIS. The integrated simulation system consists of three subsystems: a stochastic space-time rainfall model, a geographical information system (GIS), and a distributed physically-based hydrologic model. The developed stochastic space-time rainfall model is capable of estimating the storm movement and simulating a random rainfall field over a study area, based on the measurement from three raingauges. An optimization-based lag-k correlation method was developed to estimate t
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MERCURIO, Claudio. "Landslides susceptibility stochastic modelling under earthquakes and rainfalls triggering: applications to 2001 earthquakes (13th January and 13th February) and 2009 tropical storm (IDA/96E) in El Salvador." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/574407.

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Lau, Wai-hin, and 劉偉憲. "Stochastic analysis of monthly rainfall in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210387.

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Harrold, Timothy Ives Civil &amp Environmental Engineering Faculty of Engineering UNSW. "Stochastic generation of daily rainfall for catchment water management studies." Awarded by:University of New South Wales. School of Civil and Environmental Engineering, 2002. http://handle.unsw.edu.au/1959.4/18640.

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This thesis presents an approach for generating long synthetic sequences of single-site daily rainfall which can incorporate low-frequency features such as drought, while still accurately representing the day-to-day variations in rainfall. The approach is implemented in a two-stage process. The first stage is to generate the entire sequence of rainfall occurrence (i.e. whether each day is dry or wet). The second stage is to generate the rainfall amount on all wet days in the sequence. The models used in both stages are nonparametric (they make minimal general assumptions rather than specific a
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Martini, Tommaso. "statistical and probabilistic approaches to hydrological data analysis : rainfall patterns, copula-like models and first passage timeapproximations." Electronic Thesis or Diss., Pau, 2024. http://www.theses.fr/2024PAUU3051.

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L'analyse des données de précipitations et la modélisation des nombreuses variables associées sont essentielles dans des domaines tels que l'agriculture, l'écologie et l'ingénierie, et reposent sur des archives historiques en raison de la complexité des systèmes hydrologiques. Les séries de précipitations quotidiennes obtenues à partir de réseaux de pluviomètres sont parmi les plus utilisées. Un modèle fiable et flexible pour un site unique est crucial pour développer des modèles multi-sites plus complexes tenant compte des corrélations spatiales observées dans un réseau dense de stations. Com
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Smemoe, Christopher M. "Floodplain Risk Analysis Using Flood Probability and Annual Exceedance Probability Maps." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd379.pdf.

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Books on the topic "Stochastic rainfall model"

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Hromadka, Theodore V., and Robert J. Whitley. Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6.

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Hromadka, Theodore V. Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989.

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Hromadka, Theodore V. Stochastic integral equations and rainfall-runoff models. Springer-Verlag, 1989.

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Srikanthan, R. Stochastic generation of rainfall and evaporation data. Australian Govt. Pub. Service, 1985.

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Robert J. Whitley Theodore V. Hromadka II. Stochastic Integral Equations and Rainfall-Runoff Models. Springer, 1989.

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Hromadka, Theodore V. Stochastic Integral Equations and Rainfall-Runoff Models. Springer, 2012.

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Book chapters on the topic "Stochastic rainfall model"

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Hromadka, Theodore V., and Robert J. Whitley. "Rainfall-Runoff Model Criterion Variable Frequency Distributions." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_5.

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Hromadka, Theodore V., and Robert J. Whitley. "Stochastic Integral Equations Applied to a Multi-Linear Rainfall-Runoff Model." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_4.

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Hossain, Faisal, Ling Tang, Emmanouil N. Anagnostou, and Efthymios I. Nikolopoulos. "A Practical Guide to a Space-Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data." In Satellite Rainfall Applications for Surface Hydrology. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_9.

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Bell, Thomas L. "A Space-Time Stochastic Model of Rainfall for Satellite Remote-Sensing Studies." In Collected Reprint Series. American Geophysical Union, 2013. http://dx.doi.org/10.1002/9781118782071.ch9.

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Swain, Sabyasachi, S. K. Mishra, Ashish Pandey, and Deen Dayal. "A Stochastic Model-Based Monthly Rainfall Prediction Over a Large River Basin." In Sustainability of Water Resources. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13467-8_9.

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Hromadka, Theodore V., and Robert J. Whitley. "Rainfall-Runoff Aproximation." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_1.

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Hromadka, Theodore V., and Robert J. Whitley. "Using the Stochastic Integral Equation Method." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_6.

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Hromadka, Theodore V., and Robert J. Whitley. "Probability and Statistics Review." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_2.

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Hromadka, Theodore V., and Robert J. Whitley. "Introduction to Stochastic Integral Equations in Rainfall-Runoff Modeling." In Stochastic Integral Equations and Rainfall-Runoff Models. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-49309-6_3.

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Jaafar, J., A. Baki, I. A. Abu Bakar, W. Tahir, H. Awang, and F. Ismail. "Evaluation of Stochastic Daily Rainfall Data Generation Models." In ISFRAM 2015. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0500-8_17.

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Conference papers on the topic "Stochastic rainfall model"

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"Daily rainfall data infilling with a stochastic model." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.h3.jin.

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"Streamflow-based evaluation highlights discrepancies in stochastic rainfall model performance." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.nguyen509.

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Azhari, Fahimy, and Rosmiwati Mohd-Mokhtar. "Eastern Peninsula Malaysia rainfall model identification using Balanced Stochastic Realization Algorithm." In 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2015. http://dx.doi.org/10.1109/iccsce.2015.7482208.

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"Stochastic centring of BARRA rainfall space-time patterns in a hydrological model." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.scorah.

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Sirangelo, B., and D. L. De Luca. "A stochastic approach to rainfall forecasting in the space-time domain: the PRAISEST model." In RISK ANALYSIS 2006. WIT Press, 2006. http://dx.doi.org/10.2495/risk060051.

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Maimone, M., J. Rockwell, and S. Malter. "A Stochastic Rainfall Generator Approach for Applying Downscaled Global Climate Model Projections to Local Precipitation Time Series Data in Philadelphia." In World Environmental and Water Resources Congress 2017. American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784480601.022.

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Hisamatsu, Rikito, Sooyoul Kim, and Shigeru Tabeta. "Estimation of Expected Loss by Storm Surges Along Tokyo Bay Coast." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95336.

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Abstract In Japan, the fundamental disaster management plan was modified after a heavy rainfall event in 2015. According to the updated plan, the transfer of flood disaster risk to non-life insurance is promoted by the Japanese government. Thus, the importance of flood risk modeling for the insurance industry has increased. Winds are expected to become even stronger, resulting in higher storm surges, when the central pressure of the typhoon is intensified. Furthermore, it is possible for an insurance system to experience peak risk when such damage occurs simultaneously. Hence, refining the ass
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Hu, Weifei, Weiyi Chen, Xiaobo Wang, Zhenyu Liu, Jianrong Tan, and Yeqing Wang. "Wind Turbine Blade Coating Fatigue Induced by Raindrop Impact." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16510.

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Abstract With the increase of wind energy production demand, the need to manufacture larger wind turbine blades is on the rise. Because of the high tip speed of the large blade, the blade could be impacted by high-speed objects such as raindrops. This research focuses on developing a computational model for analyzing wind turbine blade coating fatigue induced by raindrop impact. A stochastic rain texture model is used to simulate a realistic rain event determined by a rain intensity and a rain duration. A smoothed particle hydrodynamic approach is implemented to calculate the impact stress con
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Dugdale, L., and I. Pattison. "Some observations of optimality in stochastic rainfall-flow models used in flow forecast." In BHS 3rd International Conference. British Hydrological Society, 2010. http://dx.doi.org/10.7558/bhs.2010.ic85.

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Young, P. C. "Some observations of optimality in stochastic rainfall-flow models used in flow forecast." In BHS 3rd International Conference. British Hydrological Society, 2010. http://dx.doi.org/10.7558/bhs.2010.ic84.

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You might also be interested in the extended bibliographies on the topic 'Stochastic rainfall model' for particular source types:
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