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Journal articles on the topic 'Stochastic disaggregation'

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1

Krzysztofowicz, Roman, and Thomas A. Pomroy. "Disaggregative Invariance of Daily Precipitation." Journal of Applied Meteorology 36, no. 6 (1997): 721–34. http://dx.doi.org/10.1175/1520-0450-36.6.721.

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Abstract Disaggregative invariance refers to stochastic independence between the total precipitation amount and its temporal disaggregation. This property is investigated herein for areal average and point precipitation amounts accumulated over a 24-h period and disaggregated into four 6-h subperiods. Statistical analyses of precipitation records from 1948 to 1993 offer convincing empirical evidence against the disaggregative invariance and in favor of the conditional disaggregative invariance, which arises when the total amount and its temporal disaggregation are conditioned on the timing of
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2

Lin, Gwo-Fong, and Fong-Chung Lee. "Multistage disaggregation processes in stochastic hydrology." Water Resources Management 6, no. 2 (1992): 101–15. http://dx.doi.org/10.1007/bf00872206.

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3

Sivakumar, B., W. W. Wallender, C. E. Puente, and M. N. Islam. "Streamflow disaggregation: a nonlinear deterministic approach." Nonlinear Processes in Geophysics 11, no. 3 (2004): 383–92. http://dx.doi.org/10.5194/npg-11-383-2004.

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Abstract. This study introduces a nonlinear deterministic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear deterministic process, rather than a stochastic process as generally assumed. The approach follows two important steps: (1) reconstruction of the scalar (streamflow) series in a multi-dimensional phase-space for representing the transformation dynamics; and (2) use of a local approximation (nearest neighbor) method for disaggregation. The approach is employed for streamflow disaggr
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4

Gao, Xiaogang, and Soroosh Sorooshian. "A Stochastic Precipitation Disaggregation Scheme for GCM Applications." Journal of Climate 7, no. 2 (1994): 238–47. http://dx.doi.org/10.1175/1520-0442(1994)007<0238:aspdsf>2.0.co;2.

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5

Buchholz, Peter. "An Aggregation/Disaggregation Algorithm for Stochastic Automata Networks." Probability in the Engineering and Informational Sciences 11, no. 2 (1997): 229–53. http://dx.doi.org/10.1017/s0269964800004782.

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Stochastic automata networks (SANs) have recently received much attention in the literature as a means to analyze complex Markov chains in an efficient way. The main advantage of SANs over most other paradigms is that they allow a very compact description of the generator matrix by means of much smaller matrices for single automata. This representation can be exploited in different iterative techniques to compute the stationary solution. However, the set of applicable solution methods for SANs is restricted, because a solution method has to respect the specific representation of the generator
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6

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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7

Gagnon, P., and A. N. Rousseau. "Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed." Hydrology and Earth System Sciences 18, no. 5 (2014): 1695–704. http://dx.doi.org/10.5194/hess-18-1695-2014.

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Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregatio
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8

Gagnon, P., and A. N. Rousseau. "Stochastic spatial disaggregation of extreme precipitation to validate a Regional Climate Model and to evaluate climate change impacts over a small watershed." Hydrology and Earth System Sciences Discussions 10, no. 6 (2013): 8167–95. http://dx.doi.org/10.5194/hessd-10-8167-2013.

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Abstract. Regional Climate Models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregatio
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9

Wright, S. E. "Primal-Dual Aggregation and Disaggregation for Stochastic Linear Programs." Mathematics of Operations Research 19, no. 4 (1994): 893–908. http://dx.doi.org/10.1287/moor.19.4.893.

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10

Schleiss, Marc, and Alexis Berne. "Stochastic Space–Time Disaggregation of Rainfall into DSD fields." Journal of Hydrometeorology 13, no. 6 (2012): 1954–69. http://dx.doi.org/10.1175/jhm-d-12-013.1.

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Abstract A stochastic method to disaggregate rain rate fields into drop size distribution (DSD) fields is proposed. It is based on a previously presented DSD simulator that has been modified to take into account prescribed block-averaged rain rate values at a coarser scale. The integral quantity used to drive the disaggregation process can be the rain rate, the radar reflectivity, or any variable directly related to the DSD. The proposed method is illustrated and qualitatively evaluated using radar rain rate data provided by MeteoSwiss for two rain events of very contrasted type (stratiform ve
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11

Grygier, Jan C., and Jery R. Stedinger. "Condensed disaggregation procedures and conservation corrections for stochastic hydrology." Water Resources Research 24, no. 10 (1988): 1574–84. http://dx.doi.org/10.1029/wr024i010p01574.

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12

Henderson, W., and D. Lucic. "Aggregation and disaggregation through insensitivity in stochastic Petri nets." Performance Evaluation 17, no. 2 (1993): 91–114. http://dx.doi.org/10.1016/0166-5316(93)90002-c.

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13

Park, Heeseong, and Gunhui Chung. "A Nonparametric Stochastic Approach for Disaggregation of Daily to Hourly Rainfall Using 3-Day Rainfall Patterns." Water 12, no. 8 (2020): 2306. http://dx.doi.org/10.3390/w12082306.

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As infrastructure and populations are highly condensed in megacities, urban flood management has become a significant issue because of the potentially severe loss of lives and properties. In the megacities, rainfall from the catchment must be discharged throughout the stormwater pipe networks of which the travel time is less than one hour because of the high impervious rate. For a more accurate calculation of runoff from the urban catchment, hourly or even sub-hourly (minute) rainfall data must be applied. However, the available data often fail to meet the hydrologic system requirements. Many
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14

Koutsoyiannis, Demetris. "A stochastic disaggregation method for design storm and flood synthesis." Journal of Hydrology 156, no. 1-4 (1994): 193–225. http://dx.doi.org/10.1016/0022-1694(94)90078-7.

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15

Cowpertwait, P. S. P., P. E. O'Connell, A. V. Metcalfe, and J. A. Mawdsley. "Stochastic point process modelling of rainfall. II. Regionalisation and disaggregation." Journal of Hydrology 175, no. 1-4 (1996): 47–65. http://dx.doi.org/10.1016/s0022-1694(96)80005-9.

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16

Clay, R. L., and I. E. Grossmann. "A disaggregation algorithm for the optimization of stochastic planning models." Computers & Chemical Engineering 21, no. 7 (1997): 751–74. http://dx.doi.org/10.1016/s0098-1354(96)00286-4.

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17

Tarboton, David G., Ashish Sharma, and Upmanu Lall. "Disaggregation procedures for stochastic hydrology based on nonparametric density estimation." Water Resources Research 34, no. 1 (1998): 107–19. http://dx.doi.org/10.1029/97wr02429.

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18

Hansen, James W., and Amor V. M. Ines. "Stochastic disaggregation of monthly rainfall data for crop simulation studies." Agricultural and Forest Meteorology 131, no. 3-4 (2005): 233–46. http://dx.doi.org/10.1016/j.agrformet.2005.06.006.

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19

Gyasi-Agyei, Yeboah. "Stochastic disaggregation of daily rainfall into one-hour time scale." Journal of Hydrology 309, no. 1-4 (2005): 178–90. http://dx.doi.org/10.1016/j.jhydrol.2004.11.018.

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20

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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21

Gyasi-Agyei, Y. "Identification of regional parameters of a stochastic model for rainfall disaggregation." Journal of Hydrology 223, no. 3-4 (1999): 148–63. http://dx.doi.org/10.1016/s0022-1694(99)00114-6.

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22

Han, Daeseok. "Stochastic Disaggregation and Aggregation of Localized Uncertainty in Pavement Deterioration Process." Journal of The Korean Society of Civil Engineers 33, no. 4 (2013): 1651–64. http://dx.doi.org/10.12652/ksce.2013.33.4.1651.

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23

Lombardo, F., E. Volpi, D. Koutsoyiannis, and F. Serinaldi. "A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall." Water Resources Research 53, no. 6 (2017): 4586–605. http://dx.doi.org/10.1002/2017wr020529.

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24

Thober, Stephan, Juliane Mai, Matthias Zink, and Luis Samaniego. "Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets." Water Resources Research 50, no. 11 (2014): 8714–35. http://dx.doi.org/10.1002/2014wr015930.

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25

Park, HeeSeong, and GunHui Chung. "Stochastic disaggregation of daily rainfall based on K-Nearest neighbor resampling method." Journal of Korea Water Resources Association 49, no. 4 (2016): 283–91. http://dx.doi.org/10.3741/jkwra.2016.49.4.283.

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26

Kottegoda, N. T., L. Natale, and E. Raiteri. "A parsimonious approach to stochastic multisite modelling and disaggregation of daily rainfall." Journal of Hydrology 274, no. 1-4 (2003): 47–61. http://dx.doi.org/10.1016/s0022-1694(02)00356-6.

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27

Gusak, Oleg, Tuğrul Dayar, and Jean-Michel Fourneau. "Iterative disaggregation for a class of lumpable discrete-time stochastic automata networks." Performance Evaluation 53, no. 1 (2003): 43–69. http://dx.doi.org/10.1016/s0166-5316(02)00227-4.

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28

Hingray, B., and M. Ben Haha. "Statistical performances of various deterministic and stochastic models for rainfall series disaggregation." Atmospheric Research 77, no. 1-4 (2005): 152–75. http://dx.doi.org/10.1016/j.atmosres.2004.10.023.

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29

Gaume, E., N. Mouhous, and H. Andrieu. "Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model." Advances in Water Resources 30, no. 5 (2007): 1301–19. http://dx.doi.org/10.1016/j.advwatres.2006.11.007.

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30

Tantanee, S., S. Patamatamakul, T. Oki, V. Sriboonlue, and T. Prempree. "Downscaled Rainfall Prediction Model (DRPM) using a Unit Disaggregation Curve (UDC)." Hydrology and Earth System Sciences Discussions 2, no. 2 (2005): 543–68. http://dx.doi.org/10.5194/hessd-2-543-2005.

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Abstract. This study was undertaken to identify the process for generating finer time scaled rainfall from higher time scaled data. The Downscaled Rainfall Prediction Model (DRPM) using the technique of unit disaggregation curve (UDC) was developed under the concept of coupling the stochastic autoregressive (AR) model with a wavelet filter and disaggregation model. Sequences of the number of rainy days and monthly rainfall were simulated from 52-year rainfall records at 4 stations in the northeastern part of Thailand. Compared with actual rainfall sequences, the 30 year generated sequences pro
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31

Molnar, Peter, and Paolo Burlando. "Preservation of rainfall properties in stochastic disaggregation by a simple random cascade model." Atmospheric Research 77, no. 1-4 (2005): 137–51. http://dx.doi.org/10.1016/j.atmosres.2004.10.024.

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32

Perera, B. J. C., and G. P. Codner. "A combined stochastic dynamic programming-statistical disaggregation approach applied to multiple reservoir systems." Water Resources Management 2, no. 3 (1988): 153–71. http://dx.doi.org/10.1007/bf00429898.

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33

Pampaloni, Matteo, Alvaro Sordo-Ward, Paola Bianucci, Ivan Gabriel-Martin, Enrica Caporali, and Luis Garrote. "A Stochastic Procedure for Temporal Disaggregation of Daily Rainfall Data in SuDS Design." Water 13, no. 4 (2021): 403. http://dx.doi.org/10.3390/w13040403.

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Hydrological design of Sustainable urban Drainage Systems (SuDS) is commonly achieved by estimating rainfall volumetric percentiles from daily rainfall series. Nevertheless, urban watersheds demand rainfall data at sub-hourly time step. Temporal disaggregation of daily rainfall records using stochastic methodologies can be applied to improve SuDS design parameters. This paper is aimed to analyze the ability of the synthetic rainfall generation process to reproduce the main characteristics of the observed rainfall and the estimation of the hydrologic parameters often used for SuDS design and by
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34

Wang, Hongxia. "Generalized Multiplicative Risk Apportionment." Risks 7, no. 2 (2019): 65. http://dx.doi.org/10.3390/risks7020065.

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This work examines apportionment of multiplicative risks by considering three dominance orderings: first-degree stochastic dominance, Rothschild and Stiglitz’s increase in risk and downside risk increase. We use the relative nth-degree risk aversion measure and decreasing relative nth-degree risk aversion to provide conditions guaranteeing the preference for “harm disaggregation” of multiplicative risks. Further, we relate our conclusions to the preference toward bivariate lotteries, which interpret correlation-aversion, cross-prudence and cross-temperance.
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35

Leurent, Fabien, Vincent Benezech, and Mahdi Samadzad. "A stochastic model of trip end disaggregation in traffic assignment to a transportation network." Procedia - Social and Behavioral Sciences 20 (2011): 485–94. http://dx.doi.org/10.1016/j.sbspro.2011.08.055.

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36

Damé, Rita de C. F., Claudia F. A. Teixeira-Gandra, Hugo A. S. Guedes, Gisele M. da Silva, and Suélen C. R. da Silveira. "Intensity-Duration-Frequency relationships: stochastic modeling and disaggregation of daily rainfall in the lagoa Mirim watershed, Rio Grande do Sul, Brazil." Engenharia Agrícola 36, no. 3 (2016): 492–502. http://dx.doi.org/10.1590/1809-4430-eng.agric.v36n3p492-502/2016.

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ABSTRACT This study aimed to investigate information gain by using rainfall intensity-duration-frequency (IDF) relationships, with data gathered within N+M years from seven rain gauge stations located in the Lagoa Mirim Watershed (South Atlantic basin). After N years of daily rainfall, the transition probabilities of a time homogeneous two-state Markov chain were defined to simulate rainfall occurrence, as well as gamma distribution to measure it; for that, daily rainfall series were composed of N+M years, with M being the generated series. The series were adjusted to Gumbel distribution, bein
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37

Llamas, J., R. Fernandez, and A. Calvache. "Génération de séries synthétiques de débit." Canadian Journal of Civil Engineering 14, no. 6 (1987): 795–806. http://dx.doi.org/10.1139/l87-118.

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The main objective of this research is to find a simple and precise methodology for the stochastic generation of flow series having statistical behaviour similar to the registered or reconstituted historical series. The common statistical parameters are the mean, the variance, the auto- and cross-correlations, and, under particular conditions, the skewness coefficient. A procedure to disaggregate annual series to lower levels (monthly or seasonal) is also presented. Finally, the article describes the general computer model utilized for the synthetic generation. Key words: synthetic generation,
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38

Gyasi-Agyei, Yeboah, and S. M. Parvez Bin Mahbub. "A stochastic model for daily rainfall disaggregation into fine time scale for a large region." Journal of Hydrology 347, no. 3-4 (2007): 358–70. http://dx.doi.org/10.1016/j.jhydrol.2007.09.047.

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39

Saada, N., M. R. Abdullah, A. Hamaideh, and A. Abu-Romman. "Application of Stochastic Analysis, Modeling and Simulation (SAMS) to Selected Hydrologic Data in the Middle East." Engineering, Technology & Applied Science Research 9, no. 3 (2019): 4261–64. http://dx.doi.org/10.48084/etasr.2750.

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Water resources in the Middle East are very scarce and the management of these resources is a challenge. In this paper, the use of stochastic analysis, modeling, and simulation (SAMS) software package to selected hydrologic data in the Middle East (namely Jordan and Saudi Arabia) are explored. Modeling and simulation experiments were conducted to test the capabilities of SAMS to be used for stochastic modeling and simulation in the Middle East region. The hydrologic data used in this study consist of historic observed rainfall data of different lengths at various sites in Jordan and Saudi Arab
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40

Hwang, S., and W. D. Graham. "Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation." Hydrology and Earth System Sciences 17, no. 11 (2013): 4481–502. http://dx.doi.org/10.5194/hess-17-4481-2013.

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Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation
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41

Nizamudeen, Zubair Ahmed, Rachael Xerri, Christopher Parmenter, et al. "Low-Power Sonication Can Alter Extracellular Vesicle Size and Properties." Cells 10, no. 9 (2021): 2413. http://dx.doi.org/10.3390/cells10092413.

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Low-power sonication is widely used to disaggregate extracellular vesicles (EVs) after isolation, however, the effects of sonication on EV samples beyond dispersion are unclear. The present study analysed the characteristics of EVs collected from mesenchymal stem cells (MSCs) after sonication, using a combination of transmission electron microscopy, direct stochastic optical reconstruction microscopy, and flow cytometry techniques. Results showed that beyond the intended disaggregation effect, sonication using the lowest power setting available was enough to alter the size distribution, membra
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42

Abdellatif, M., W. Atherton, and R. Alkhaddar. "Application of the stochastic model for temporal rainfall disaggregation for hydrological studies in north western England." Journal of Hydroinformatics 15, no. 2 (2012): 555–67. http://dx.doi.org/10.2166/hydro.2012.090.

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Assessment of climate change on any hydrological system requires higher temporal resolution at hourly or less in terms of time scale. This paper implements the Bartlett–Lewis Rectangular Pulses (BLRP) model coupled with a proportional adjusting procedure to disaggregate daily rainfall to hourly rainfall in order to demonstrate the reliability of this method. Three stations in northwestern England have been selected that represent different climates in the region. Parameters estimation of the BLRP model has been performed under different levels of hourly rainfall aggregation for a combination o
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43

Marek, Ivo, and Petr Mayer. "Convergence analysis of an iterative aggregation/disaggregation method for computing stationary probability vectors of stochastic matrices." Numerical Linear Algebra with Applications 5, no. 4 (1998): 253–74. http://dx.doi.org/10.1002/(sici)1099-1506(199807/08)5:4<253::aid-nla124>3.0.co;2-b.

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44

Gagnon, Patrick, Alain N. Rousseau, Dominique Charron, Vincent Fortin, and René Audet. "The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada." Journal of Hydrology 554 (November 2017): 507–16. http://dx.doi.org/10.1016/j.jhydrol.2017.08.023.

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45

Mishra, Ashok K., Amor V. M. Ines, Vijay P. Singh, and James W. Hansen. "Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation." Stochastic Environmental Research and Risk Assessment 27, no. 2 (2012): 449–57. http://dx.doi.org/10.1007/s00477-012-0667-9.

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46

Evin, Guillaume, Anne-Catherine Favre, and Benoit Hingray. "Stochastic generation of multi-site daily precipitation focusing on extreme events." Hydrology and Earth System Sciences 22, no. 1 (2018): 655–72. http://dx.doi.org/10.5194/hess-22-655-2018.

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Abstract. Many multi-site stochastic models have been proposed for the generation of daily precipitation, but they generally focus on the reproduction of low to high precipitation amounts at the stations concerned. This paper proposes significant extensions to the multi-site daily precipitation model introduced by Wilks, with the aim of reproducing the statistical features of extremely rare events (in terms of frequency and magnitude) at different temporal and spatial scales. In particular, the first extended version integrates heavy-tailed distributions, spatial tail dependence, and temporal
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47

Hwang, S., and W. D. Graham. "Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation." Hydrology and Earth System Sciences Discussions 10, no. 2 (2013): 2141–81. http://dx.doi.org/10.5194/hessd-10-2141-2013.

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Abstract. There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (bias-correction and stochastic analog method, BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve the spatial autocorrelation str
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48

Hingray, B., E. Monbaron, I. Jarrar, A. C. Favre, D. Consuegra, and A. Musy. "Stochastic generation and disaggregation of hourly rainfall series for continuous hydrological modelling and flood control reservoir design." Water Science and Technology 45, no. 2 (2002): 113–19. http://dx.doi.org/10.2166/wst.2002.0035.

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In the urban environment, stormwater detention basins are a powerful means to limit the frequency of sewer system failures and consecutive urban flooding. To design such waterworks or to check their efficiency, it is possible to carry out continuous rainfall-runoff modelling. A long-term discharge series obtained from a long-term rainfall series is used as input for a storage model describing the detention basin behaviour: the basin behaviour may be consequently studied over a long period. The provided statistical information on the working state frequency, failure frequency, … of the detentio
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49

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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50

Marek, Ivo, and Petr Mayer. "Convergence theory of some classes of iterative aggregation/disaggregation methods for computing stationary probability vectors of stochastic matrices." Linear Algebra and its Applications 363 (April 2003): 177–200. http://dx.doi.org/10.1016/s0024-3795(02)00333-6.

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