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Journal articles on the topic 'Stochastic rainfall model'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

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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Pearce, Andrew J. "A stochastic model of rainfall interception — Comment." Journal of Hydrology 89, no. 3-4 (1987): 371–72. http://dx.doi.org/10.1016/0022-1694(87)90187-9.

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Haberlandt, Uwe. "Stochastic Rainfall Synthesis Using Regionalized Model Parameters." Journal of Hydrologic Engineering 3, no. 3 (1998): 160–68. http://dx.doi.org/10.1061/(asce)1084-0699(1998)3:3(160).

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Cho. "Analysis of the Applicability of Parameter Estimation Methods for a Stochastic Rainfall Model." Journal of the Korean Society of Civil Engineers 34, no. 4 (2014): 1105. http://dx.doi.org/10.12652/ksce.2014.34.4.1105.

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Fikri, Agung Muhammad, and Aceng Komarudin Mutaqin. "Penerapan Model Pembangkit Curah Hujan Stokastik untuk Simulasi Curah Hujan Harian di Stasiun Badan Meteorologi Klimatologi dan Geofisika (BMKG) Kertajati Jawa Barat." Bandung Conference Series: Statistics 2, no. 2 (2022): 79–86. http://dx.doi.org/10.29313/bcss.v2i2.3220.

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Abstract. Optimal management of water resources can have big implications for a country. The main input source in the water resources system is rainfall. Rainfall data is an important component in determining water resource planning. However, in the recording there is often an unavailability of rainfall data. This unavailability of rainfall data can be generated by stochastic analysis. One of the stochastic methods that is often used by other researchers is the stochastic rainfall generator model. This model is a stochastic model that uses historical meteorology and the nature of the stochasti

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Willems, P. "Stochastic generation of spatial rainfall for urban drainage areas." Water Science and Technology 39, no. 9 (1999): 23–30. http://dx.doi.org/10.2166/wst.1999.0433.

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Data from a dense network of rain gauges in the city of Antwerp (Belgium) has been used to study the stochastic structure of spatial rainfall at the small spatial scale of small hydrographic or urban catchments. The derived spatial rainfall model contains two structures: a deterministic structure for the physical description of individual rain cells and cell clusters, and a stochastic structure for the description of the intrinsic randomness in the sequence of different rain events. Such a model forms the basis of the stochastic generation of spatial rainfall for urban catchments. Spatial rain

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Lee, Jeonghoon, and Sangdan Kim. "Temporal Disaggregation of Daily Rainfall data using Stochastic Point Rainfall Model." Journal of the Korean Society of Hazard Mitigation 18, no. 2 (2018): 493–503. http://dx.doi.org/10.9798/kosham.2018.18.2.493.

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Lee, Yongdae, Sheung-Kown Kim, and Ick Hwan Ko. "Multistage stochastic linear programming model for daily coordinated multi-reservoir operation." Journal of Hydroinformatics 10, no. 1 (2008): 23–41. http://dx.doi.org/10.2166/hydro.2008.007.

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Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenario-based stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span. Fut

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Lee, Jeonghoon, Ungtae Kim, Sangdan Kim, and Jungho Kim. "Development and Application of a Rainfall Temporal Disaggregation Method to Project Design Rainfalls." Water 14, no. 9 (2022): 1401. http://dx.doi.org/10.3390/w14091401.

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A climate model is essential for hydrological designs considering climate change, but there are still limitations in employing raw temporal and spatial resolutions for small urban areas. To solve the temporal scale gap, a temporal disaggregation method of rainfall data was developed based on the Neyman–Scott Rectangular Pulse Model, a stochastic rainfall model, and future design rainfall was projected. The developed method showed better performance than the benchmark models. It produced promising results in estimating the rainfall quantiles for recurrence intervals of less than 20 years. Overa

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Sirangelo, B., P. Versace, and D. L. De Luca. "Rainfall nowcasting by at site stochastic model P.R.A.I.S.E." Hydrology and Earth System Sciences Discussions 4, no. 1 (2007): 151–77. http://dx.doi.org/10.5194/hessd-4-151-2007.

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Abstract. The paper introduces a stochastic model to forecast rainfall heights at site: the P.R.A.I.S.E.~model (Prediction of Rainfall Amount Inside Storm Events). PRAISE is based on the assumption that the rainfall height Hi+1 accumulated on an interval Δt between the instants iΔt and (i+1)Δt is correlated with a variable Zi(ν), representing antecedent precipitation. The mathematical background is given by a joined probability density fHi+1 Zi( ν) (hi+1 ,zi(ν)) in which the variables have a mixed nature, that is a finite probability in correspondence to the null value and infinitesimal probab

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Sirangelo, B., P. Versace, and D. L. De Luca. "Rainfall nowcasting by at site stochastic model P.R.A.I.S.E." Hydrology and Earth System Sciences 11, no. 4 (2007): 1341–51. http://dx.doi.org/10.5194/hess-11-1341-2007.

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Abstract. The paper introduces a stochastic model to forecast rainfall heights at site: the P.R.A.I.S.E. model (Prediction of Rainfall Amount Inside Storm Events). PRAISE is based on the assumption that the rainfall height Hi+1 accumulated on an interval Δt between the instants iΔt and (i+1Δt is correlated with a variable Zi(ν), representing antecedent precipitation. The mathematical background is given by a joined probability density fHi+1, Zi(ν)(hi+1 ,zi(ν)) in which the variables have a mixed nature, that is a finite probability in correspondence to the null value and infinitesimal probabil

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Brigandì, Giuseppina, and Giuseppe T. Aronica. "Generation of Sub-Hourly Rainfall Events through a Point Stochastic Rainfall Model." Geosciences 9, no. 5 (2019): 226. http://dx.doi.org/10.3390/geosciences9050226.

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The aim of this paper is to present a stochastic model to generate sub-hourly rainfall events at a given point. Historical events used as the input have been extracted by the sub-hourly rainfall series available for a defined rain gauge station based on a fixed inter-event time and selected if their average intensity was larger than a critical fixed one. The sub-hourly events generated by applying the proposed methodology are completely stochastic and their main characteristics, i.e., shape, duration and average intensity, have been derived as a function of the statistics of the historical eve

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Lanza, L. G. "A conditional simulation model of intermittent rain fields." Hydrology and Earth System Sciences 4, no. 1 (2000): 173–83. http://dx.doi.org/10.5194/hess-4-173-2000.

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Abstract. The synthetic generation of random fields with specified probability distribution, correlation structure and probability of no-rain areas is used as the basis for the formulation of a stochastic space-time rainfall model conditional on rain gauge observations. A new procedure for conditioning while preserving intermittence is developed to provide constraints to Monte Carlo realisations of possible rainfall scenarios. The method addresses the properties of the convolution operator involved in generating random field realisations and is actually independent of the numerical algorithm u

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Dwivedi, D. K., J. H. Kelaiya, and G. R. Sharma. "Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: A case study of Junagadh, Gujarat, India." Journal of Applied and Natural Science 11, no. 1 (2019): 35–41. http://dx.doi.org/10.31018/jans.v11i1.1951.

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The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Time series modelling has been extensively used in stochastic hydrology for predicting various hydrological processes. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time lagged models extract maximum possible information from the available record for forecasting. Artificial neural network has been found to be

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Mellor, D., J. Sheffield, P. E. O'Connell, and A. V. Metcalfe. "A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator." Hydrology and Earth System Sciences 4, no. 4 (2000): 603–15. http://dx.doi.org/10.5194/hess-4-603-2000.

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Abstract. The need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justification for the choice of the Modified Turning Bands (MTB) model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinct scales: raincells, cluster potential regions, rainbands and the

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Zhou, Yan, Zhongmin Liang, Binquan Li, Yixin Huang, Kai Wang, and Yiming Hu. "Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model." Sustainability 13, no. 6 (2021): 3588. http://dx.doi.org/10.3390/su13063588.

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Rainfall is an important input to conceptual hydrological models, and its accuracy would have a considerable effect on that of the model simulations. However, traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs without recognizing its spatial variability. To solve this, a seamless integration framework that couples rainfall spatial variability with a conceptual rainfall-runoff model, named the statistical rainfall-runoff (SRR) model, is built in this study. In the SRR model, the exponential difference distribution (EDD) is proposed to describe the sp

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Thauvin, V., E. Gaume, and C. Roux. "A short time-step point rainfall stochastic model." Water Science and Technology 37, no. 11 (1998): 37–45. http://dx.doi.org/10.2166/wst.1998.0431.

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This paper deals with the development of a point rainfall stochastic model, which generates synthetic sequences of 5 min rainfall rates. This time step is necessary to derive correctly the short hydrological response of urban catchments. The model simulates dry and rainy sequences in alternance. Particular attention has been paid to intense periods which are of utmost importance as far as flooding is concerned. This leads us to describe a rain event as a juxtaposition of inter-showers and showers. The intensity of an inter-shower is assumed to remain constant. Showers are defined as a successi

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Bo, Zhiquan, Shafiqul Islam, and E. A. B. Eltahir. "Aggregation-disaggregation properties of a stochastic rainfall model." Water Resources Research 30, no. 12 (1994): 3423–35. http://dx.doi.org/10.1029/94wr02026.

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(Sri) Srikanthan, Ratnasingham, and Geoffrey G. S. Pegram. "A nested multisite daily rainfall stochastic generation model." Journal of Hydrology 371, no. 1-4 (2009): 142–53. http://dx.doi.org/10.1016/j.jhydrol.2009.03.025.

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Rebora, N., L. Ferraris, J. von Hardenberg, and A. Provenzale. "Rainfall downscaling and flood forecasting: a case study in the Mediterranean area." Natural Hazards and Earth System Sciences 6, no. 4 (2006): 611–19. http://dx.doi.org/10.5194/nhess-6-611-2006.

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Abstract. The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff models. In this work we present an application of a new spatial-temporal downscaling procedure, called RainFARM, to an intense precipitation event predicted by the limited-area meteorological model

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Březková, L., M. Starý, and P. Doležal. "The real-time stochastic flow forecast." Soil and Water Research 5, No. 2 (2010): 49–57. http://dx.doi.org/10.17221/13/2009-swr.

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In the Czech Republic, deterministic flow forecasts with the lead time of 48 hours, calculated by rainfall-runoff models for basins of a size of several hundreds to thousands square kilometers, are nowadays a common part of the operational hydrological service. The Czech Hydrometeorological Institute (CHMI) issues daily the discharge forecast for more than one hundred river profiles. However, the causal rainfall is a random process more than a deterministic one, therefore the deterministic discharge forecast based on one precipitation prediction is a significant simplification of the reality.

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Andrés-Doménech, I., A. Montanari, and J. B. Marco. "Stochastic rainfall analysis for storm tank performance evaluation." Hydrology and Earth System Sciences Discussions 7, no. 2 (2010): 1849–81. http://dx.doi.org/10.5194/hessd-7-1849-2010.

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Abstract. Stormwater detention tanks are widely used for mitigating impacts of combined sewer overflows (CSO) from urban catchments into receiving water bodies. The optimal size of detention tanks depends on climate and sewer system behaviours and can be estimated by using derived distribution approaches. They are based on using a stochastic model to fit the statistical pattern of observed rainfall records and a urban hydrology model to transform rainfall in sewer discharge. A key issue is the identification of the optimal structure of the stochastic rainfall model. Point processes are frequen

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Andrés-Doménech, I., A. Montanari, and J. B. Marco. "Stochastic rainfall analysis for storm tank performance evaluation." Hydrology and Earth System Sciences 14, no. 7 (2010): 1221–32. http://dx.doi.org/10.5194/hess-14-1221-2010.

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Abstract. Stormwater detention tanks are widely used for mitigating impacts of combined sewer overflows (CSO) from urban catchments into receiving water bodies. The optimal size of detention tanks depends on climate and sewer system behaviours and can be estimated by using derived distribution approaches. They are based on using a stochastic model to fit the statistical pattern of observed rainfall records and a urban hydrology model to transform rainfall in sewer discharge. A key issue is the identification of the optimal structure of the stochastic rainfall model. Point processes are frequen

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Hottovy, Scott, and Samuel N. Stechmann. "Rain process models and convergence to point processes." Nonlinear Processes in Geophysics 30, no. 1 (2023): 85–100. http://dx.doi.org/10.5194/npg-30-85-2023.

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Abstract. A variety of stochastic models have been used to describe time series of precipitation or rainfall. Since many of these stochastic models are simplistic, it is desirable to develop connections between the stochastic models and the underlying physics of rain. Here, convergence results are presented for such a connection between two stochastic models: (i) a stochastic moisture process as a physics-based description of atmospheric moisture evolution and (ii) a point process for rainfall time series as spike trains. The moisture process has dynamics that switch after the moisture hits a

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Musakkir, Nurul Azizah, Nurtiti Sunusi, and Sri Astuti Thamrin. "Stochastic Model of the Annual Maximum Rainfall Series Using Probability Distributions." Malaysian Journal of Fundamental and Applied Sciences 19, no. 5 (2023): 827–39. http://dx.doi.org/10.11113/mjfas.v19n5.2945.

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Rainfall is a natural process that is often characterized by significant variability and uncertainty. Stochastic models of rainfall typically involve the use of probability distributions to describe the likelihood of different outcomes occurring. This study aimed to model the annual maximum of daily rainfall in Makassar City, Indonesia for the period 1980–2022, specifically focusing on the rainy season (November to April) using probability distributions to estimate return periods. The study used the Generalized Extreme Value (GEVD) and Gumbel distributions. The Kolmogorov-Smirnov test was used

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Sirangelo, B., E. Ferrari, and D. L. De Luca. "Occurrence analysis of daily rainfalls through non-homogeneous Poissonian processes." Natural Hazards and Earth System Sciences 11, no. 6 (2011): 1657–68. http://dx.doi.org/10.5194/nhess-11-1657-2011.

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Abstract. A stochastic model based on a non-homogeneous Poisson process, characterised by a time-dependent intensity of rainfall occurrence, is employed to explain seasonal effects of daily rainfalls exceeding prefixed threshold values. The data modelling has been performed with a partition of observed daily rainfall data into a calibration period for parameter estimation and a validation period for checking on occurrence process changes. The model has been applied to a set of rain gauges located in different geographical areas of Southern Italy. The results show a good fit for time-varying in

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Rulli, M. C., and R. Rosso. "An integrated simulation method for flash-flood risk assessment: 1. Frequency predictions in the Bisagno River by combining stochastic and deterministic methods." Hydrology and Earth System Sciences 6, no. 2 (2002): 267–84. http://dx.doi.org/10.5194/hess-6-267-2002.

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Abstract. A stochastic rainfall generator and a deterministic rainfall-runoff model, both distributed in space and time, are combined to provide accurate flood frequency prediction in the Bisagno River basin (Thyrrenian Liguria, N.W. Italy). The inadequacy of streamflow records with respect to the return period of the required flow discharges makes the stochastic simulation methodology a useful operational alternative to a regionalisation procedure for flood frequency analysis and derived distribution techniques. The rainfall generator is the Generalized Neyman-Scott Rectangular Pulses (GNSRP)

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Wu, Shiang-Jen, Chih-Tsu Hsu, and Che-Hao Chang. "Stochastic modeling of gridded short-term rainstorms." Hydrology Research 52, no. 4 (2021): 876–904. http://dx.doi.org/10.2166/nh.2021.002.

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Abstract This study aims to develop a stochastic method (SM_GSTR) for generating short-time (i.e., hourly) rainstorm events at all grids (named gridded rainstorm events) in a region. The proposed SM_GSTR model is developed by the non-normal correlated multivariate Monte Carlo simulation (MMCS) method with the statistical properties and spatiotemporal correlation structures of the four event-based gridded rainfall characteristics. The radar-based rainfall data on 20 typhoon events at 336 grids in a basin located in north Taiwan, Nankan River watershed, are used in the model development and demo

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Todini, E., and M. di Bacco. "A combined Pòlya process and mixture distribution approach to rainfall modelling." Hydrology and Earth System Sciences 1, no. 2 (1997): 367–78. http://dx.doi.org/10.5194/hess-1-367-1997.

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Abstract. A new probabilistic interpretation of at site rainfall sequences is introduced for the development of a stochastic model of rain. The model, is divided into two sub models; the first one describing the total number of rainfall spells within a window of time is described by a Pòlya process in order to reproduce better the variable probability of occurrence of rainfall during storm events (due to the presence of different numbers of rainfall cells); the second sub model, conditional on the first one, describes the total quantity of rainfall in the time window, given a number of rainfal

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Portoghese, I., E. Bruno, N. Guyennon, and V. Iacobellis. "Stochastic bias-correction of daily rainfall scenarios for hydrological applications." Natural Hazards and Earth System Sciences 11, no. 9 (2011): 2497–509. http://dx.doi.org/10.5194/nhess-11-2497-2011.

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Abstract. The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge. In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was anal

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Cowpertwait, P. S. P. "Mixed rectangular pulses models of rainfall." Hydrology and Earth System Sciences 8, no. 5 (2004): 993–1000. http://dx.doi.org/10.5194/hess-8-993-2004.

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Abstract. A stochastic rainfall model, obtained as the superposition of independent Neyman-Scott Rectangular Pulses (NSRP), is proposed to provide a flexible parameterisation and general procedure for modelling rainfall. The methodology is illustrated using hourly data from Auckland, New Zealand, where the model is fitted to data collected for each calendar month over the period: 1966–1998. For data taken over the months April to August, two independent superposed NSRP processes are fitted, which may correspond to the existence of mixtures of convective and stratiform storm types for these mon

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Baigorria, Guillermo A., and James W. Jones. "GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data." Journal of Climate 23, no. 22 (2010): 5990–6008. http://dx.doi.org/10.1175/2010jcli3537.1.

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Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed

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Fadhil, R. M., M. K. Rowshon, D. Ahmad, A. Fikri, and W. Aimrun. "A stochastic rainfall generator model for simulation of daily rainfall events in Kurau catchment: model testing." Acta Horticulturae, no. 1152 (March 2017): 1–10. http://dx.doi.org/10.17660/actahortic.2017.1152.1.

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Rigby, J., and A. Porporato. "Simplified stochastic soil moisture models: a look at infiltration." Hydrology and Earth System Sciences Discussions 3, no. 4 (2006): 1339–67. http://dx.doi.org/10.5194/hessd-3-1339-2006.

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Abstract. A simplified, vertically averaged model of soil moisture interpreted at the daily time scale and forced by a stochastic process of instantaneous rainfall events is compared with a model which uses a non-overlapping rectangular pulse rainfall model and a more physically based description of infiltration. The models are compared with respect to the importance of short time-scale (intra-storm) variable infiltration in determining soil moisture dynamics at the daily time-scale. Differences in approach to infiltration modelling show only minor effects on the probabilistic structure of soi

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Smith, James A., and Richard D. De Veaux. "A stochastic model relating rainfall intensity to raindrop processes." Water Resources Research 30, no. 3 (1994): 651–64. http://dx.doi.org/10.1029/93wr02349.

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PATHIRANA, Assela, Srikanth HEARTH, and Katumi MUSIAKE. "ON THE SCALING PROPERTIES OF A STOCHASTIC RAINFALL MODEL." PROCEEDINGS OF HYDRAULIC ENGINEERING 44 (2000): 1–6. http://dx.doi.org/10.2208/prohe.44.1.

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Thayakaran, R., and N. I. Ramesh. "Doubly stochastic Poisson pulse model for fine-scale rainfall." Stochastic Environmental Research and Risk Assessment 31, no. 3 (2016): 705–24. http://dx.doi.org/10.1007/s00477-016-1270-2.

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Ramesh, N. I., A. P. Garthwaite, and C. Onof. "A doubly stochastic rainfall model with exponentially decaying pulses." Stochastic Environmental Research and Risk Assessment 32, no. 6 (2017): 1645–64. http://dx.doi.org/10.1007/s00477-017-1483-z.

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Cameron, D., K. Beven, J. Tawn, and P. Naden. "Flood frequency estimation by continuous simulation (with likelihood based uncertainty estimation)." Hydrology and Earth System Sciences 4, no. 1 (2000): 23–34. http://dx.doi.org/10.5194/hess-4-23-2000.

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Abstract. A continuous simulation methodology, which incorporates the quantification of modelling uncertainties, is used for flood frequency estimation. The methodology utilises the rainfall-runoff model TOPMODEL within the uncertainty framework of GLUE. Long return period estimates are obtained through the coupling of a stochastic rainfall generator with TOPMODEL. Examples of applications to four gauged UK catchments are provided. A comparison with a traditional statistical approach indicates the suitability of the methodology as an alternative technique for flood frequency estimation. It is

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RITTER, WALTER, PEDRO MOSINO, and ENRIQUE BUENDIA. "Dynamic rain model for linear stochastic environments." MAUSAM 49, no. 1 (2021): 127–34. http://dx.doi.org/10.54302/mausam.v49i1.3606.

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To develop modem agriculture, a vision of an integral management is required, where the complexity of interactions between climatic, biological, economical, social and political factors involved in the food production must systematically be analyzed in a context of regional conditions.  At the same time, it is necessary to develop the ability to forecast both the climatic variations and their possible impact on society. The minimization of this impact on agriculture through consistent practices adequate to local climates, is not only commendable, but basically necessary, besides, t

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Khouider, Boualem, C. T. Sabeerali, R. S. Ajayamohan, et al. "A Novel Method for Interpolating Daily Station Rainfall Data Using a Stochastic Lattice Model." Journal of Hydrometeorology 21, no. 5 (2020): 909–33. http://dx.doi.org/10.1175/jhm-d-19-0143.1.

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AbstractRain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately,

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