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Journal articles on the topic 'Stochastic disaggregation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Krzysztofowicz, Roman, and Thomas A. Pomroy. "Disaggregative Invariance of Daily Precipitation." Journal of Applied Meteorology 36, no. 6 (June 1, 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 precipitation within the diurnal cycle. The property of conditional disaggregative invariance allows the modeler or the forecaster to decompose the problem of quantitative precipitation forecasting into three tasks: (i) forecasting the precipitation timing; (ii) forecasting the total amount, conditional on timing; and (iii) forecasting the temporal disaggregation, conditional on timing. Tasks (ii) and (iii) can be performed independently of one another, and this offers a formidable advantage for applications.

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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 (September 8, 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 disaggregation in the Mississippi River basin, USA. Data of successively doubled resolutions between daily and 16 days (i.e. daily, 2-day, 4-day, 8-day, and 16-day) are studied, and disaggregations are attempted only between successive resolutions (i.e. 2-day to daily, 4-day to 2-day, 8-day to 4-day, and 16-day to 8-day). Comparisons between the disaggregated values and the actual values reveal excellent agreements for all the cases studied, indicating the suitability of the approach for streamflow disaggregation. A further insight into the results reveals that the best results are, in general, achieved for low embedding dimensions (2 or 3) and small number of neighbors (less than 50), suggesting possible presence of nonlinear determinism in the underlying transformation process. A decrease in accuracy with increasing disaggregation scale is also observed, a possible implication of the existence of a scaling regime in streamflow.

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4

Gao, Xiaogang, and Soroosh Sorooshian. "A Stochastic Precipitation Disaggregation Scheme for GCM Applications." Journal of Climate 7, no. 2 (February 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 (April 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 matrix to exploit the compact representation. In particular, aggregation/disaggregation (a/d) methods cannot be applied in their usual realization for SANs without losing the possibility to exploit the compact representation of the generator matrix.In this paper, a new a/d algorithm for SANs is introduced. The algorithm differs significantly from standard a/d methods because the parts to be aggregated are defined in a completely different way, exploiting the structure of the generator matrix of a SAN. Aggregation is performed with respect to single automata or sets of automata, which are the basic parts generating a SAN. It is shown that the new algorithm is efficient even if the automata are not loosely coupled.

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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 (December 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 (May 9, 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 disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy – CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

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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 (June 26, 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 disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value. The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation: (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961–2099 are used over a small watershed (130 km2) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy (CAPE)), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation permits to determine that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

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9

Wright, S. E. "Primal-Dual Aggregation and Disaggregation for Stochastic Linear Programs." Mathematics of Operations Research 19, no. 4 (November 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 (December 1, 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 versus convective). The evaluation shows that both types of rainfall are correctly disaggregated, although the general agreement in terms of rain rate distributions, intermittency, and space–time structures is much better for the stratiform case. Possible extensions and generalizations of the technique (e.g., using radar reflectivities at two different frequencies or polarizations to drive the disaggregation process) are discussed at the end of the paper.

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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 (October 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 (March 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 (August 17, 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 studies have been conducted to disaggregate time-series data while preserving distributional statistics from observed data. The K-nearest neighbor resampling (KNNR) method is a useful application of the nonparametric disaggregation technique. However, it is not easy to apply in the disaggregation of daily rainfall data into hourly while preserving statistical properties and boundary continuity. Therefore, in this study, three-day rainfall patterns were proposed to improve reproducible ability of statistics. Disaggregated rainfall was resampled only from a group having the same three-day rainfall patterns. To show the applicability of the proposed disaggregation method, probability distribution and L-moment statistics were compared. The proposed KNNR method with three-day rainfall patterns reproduced better the characteristics of rainfall event such as event duration, inter-event time, and toral amount of rainfall event. To calculate runoff from urban catchment, rainfall event is more important than hourly rainfall depth itself. Therefore, the proposed stochastic disaggregation method is useful to hydrologic analysis, particularly in rainfall disaggregation.

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14

Koutsoyiannis, Demetris. "A stochastic disaggregation method for design storm and flood synthesis." Journal of Hydrology 156, no. 1-4 (April 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 (February 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 (March 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 (January 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 (August 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 (July 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 (February 28, 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 (October 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 (July 30, 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 (June 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 (November 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 (April 30, 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 (April 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 (June 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 (September 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 (May 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 (April 14, 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 provided R-square values of 0.47-0.60. The model was applied to forecast the number of rainy days and monthly rainfall for the year of 2002. When compared with actual records the prediction model provided R-square values of 0.50 to 0.79.

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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 (September 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 (February 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 using the generally available daily rainfall data. Other specifics objectives are to analyze the effect of Minimum Inter-event Time (MIT) and storm volume threshold on rainfall volumetric percentiles commonly used in SuDS design. The reliability of the stochastic spatial-temporal model RainSim V.3 to reproduce observed key characteristics of rainfall pattern and volumetric percentiles, was also investigated. Observed and simulated continuous rainfall series with sub-hourly time-step were used to calculate four key characteristics of rainfall and two types of rainfall volumetric percentiles. To separate independent rainstorm events, MIT values of 3, 6, 12, 24, 48 and 72 h and storm volume thresholds of 0.2, 0.5, 1 and 2 mm were considered. Results show that the proposed methodology improves the estimation of the key characteristics of the rainfall events as well as the hydrologic parameters for SuDS design, compared with values directly deduced from the observed rainfall series with daily time-step. Moreover, MITs rainfall volumetric percentiles of total number of rainfall events are very sensitive to MIT and threshold values, while percentiles of total volume of accumulated rainfall series are sensitive only to MIT values.

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34

Wang, Hongxia. "Generalized Multiplicative Risk Apportionment." Risks 7, no. 2 (June 12, 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 (June 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, being used in annual maximum daily rainfall disaggregation for durations of 10, 20, 30, 40, 50, 60, 120, 360, 720 and 1440 min. Daily rainfall disaggregation was validated through IDF relationships taken from pluviograph records of N years and from N+M years, using the “t” test of relative mean squared error. We can infer that there was information gain using IDF relationships of rainfall occurrence when using N years of observed data and M years of generated data by stochastic modeling compared to those obtained from a composed series of N years.

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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 (December 1, 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, flow series, disaggregation, computer model.

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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 (December 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 (June 8, 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 Arabia. The models used in this study include: autoregressive moving average (ARMA) models, periodic autoregressive moving average (PARMA) models, multi-site contemporaneous autoregressive moving average (CARMA) models, and temporal disaggregation models. Results indicate that SAMS can be used as a tool for stochastic modeling and simulation of hydrologic data in Jordan and Saudi Arabia. It is important for managers and decision makers of water resources in these countries to be able to use sophisticated tools such as SAMS while deciding water management policies in these countries.

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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 (November 13, 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 structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

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41

Nizamudeen, Zubair Ahmed, Rachael Xerri, Christopher Parmenter, Kiran Suain, Robert Markus, Lisa Chakrabarti, and Virginie Sottile. "Low-Power Sonication Can Alter Extracellular Vesicle Size and Properties." Cells 10, no. 9 (September 14, 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, membrane integrity, and uptake of EVs in cultured cells. These results point to the need for a more systematic analysis of sonication procedures to improve reproducibility in EV-based cellular experiments.

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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 (December 27, 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 of rainfall statistics. The HYETOS model, which applies BLRP, reproduced standard statistics such as mean, variance, Lag-1, autocorrelation as well as dry proportions. Moreover, the model was proven to have the capability to disaggregate the rainfall extremes. The fitted BLRP model could then be used to disaggregate future daily rainfall in order to investigate the climate change impact of different rainfall intensities.

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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 (July 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 (November 21, 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 (January 25, 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 dependence in order to obtain a robust and appropriate representation of the most extreme precipitation fields. A second version enhances the first version using a disaggregation method. The performance of these models is compared at different temporal and spatial scales on a large region covering approximately half of Switzerland. While daily extremes are adequately reproduced at the stations by all models, including the benchmark Wilks version, extreme precipitation amounts at larger temporal scales (e.g., 3-day amounts) are clearly underestimated when temporal dependence is ignored.

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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 (February 20, 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 structure of observed daily precipitation sequences. This approach was designed to reproduce observed spatial and temporal variability as well as mean climatology. We used the BCSA method to downscale 4 GCM precipitation predictions from 1961 to 1999 over the state of Florida and compared the skill of the method to the results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, bias-correction and constructed analog (BCCA) method, and a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC). Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean climatology of daily precipitation while the BCSD, SDBC and BCSA methods accurately reproduced it, (2) the BCSD and BCCA methods underestimated temporal variability because of the interpolation and regression schemes used for downscaling and thus, did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in precipitation resulting in under-prediction of spatial variance and over-prediction of spatial correlation, whereas the new stochastic technique (BCSA) accurately reproduces observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be accurately predicted. For low-relief, rainfall-dominated watersheds where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

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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 (January 1, 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 detention basin is of high interest for the basin diagnostic or for its design. This paper presents the whole methodology which leads to production of such statistical information and especially: the models used to generate long term rainfall series with a short time step, the rainfall-runoff model used to transform the later series into a long term discharge series, and the model used to describe the behaviour of the detention basin. This methodology was applied to evaluate the efficiency of 4 detention basins built for stormwater control and flood mitigation. They are situated on a Swiss urban catchment (Chamberonne catchment – 40 km2) collecting water from the Mèbre and Sorge rivers.

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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 (August 1, 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 properties that are consistent with those of the measured precipitation fields. The application of the disaggregation procedure to an example meteorological forecast illustrates how the method can be implemented in operational practice.

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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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