A nonlinear stochastic model describing the formation of daily and mean monthly water flow in river basins

2007 ◽  
Vol 34 (6) ◽  
pp. 624-634 ◽  
Author(s):  
B. M. Dolgonosov ◽  
K. A. Korchagin
1984 ◽  
Vol 20 (2) ◽  
pp. 297-309 ◽  
Author(s):  
Srinivas G. Rao ◽  
Ramachandra A. Rao

2020 ◽  
Vol 11 (2) ◽  
pp. 111
Author(s):  
Pooria Hashemzahi ◽  
Amirhossein Azadnia ◽  
Masoud Rahiminezhad Galankashi ◽  
Syed Ahmad Helmi ◽  
Farimah Mokhatab Rafiei

1996 ◽  
Vol 77 (16) ◽  
pp. 3280-3283 ◽  
Author(s):  
A. N. Drozdov ◽  
M. Morillo

2005 ◽  
Vol 62 (7) ◽  
pp. 2098-2117 ◽  
Author(s):  
Judith Berner

Abstract To link prominent nonlinearities in the dynamics of 500-hPa geopotential heights to non-Gaussian features in their probability density, a nonlinear stochastic model of atmospheric planetary wave behavior is developed. An analysis of geopotential heights generated by extended integrations of a GCM suggests that a stochastic model and its associated Fokker–Planck equation call for a nonlinear drift and multiplicative noise. All calculations are carried out in the reduced phase space spanned by the leading EOFs. It is demonstrated that this nonlinear stochastic model of planetary wave behavior captures the non-Gaussian features in the probability density function of atmospheric states to a remarkable degree. Moreover, it not only predicts global temporal characteristics, but also the nonlinear, state-dependent divergence of state trajectories. In the context of this empirical modeling, it is discussed on which time scale a stochastic model is expected to approximate the behavior of a continuous deterministic process. The reduced model is then used to determine the importance of the nonlinearities in the drift and the role of the multiplicative noise. While the nonlinearities in the drift are crucial for a good representation of planetary wave behavior, multiplicative (i.e., state dependent) noise is not absolutely essential. It is found that a major contributor to the stochastic component is the Branstator–Kushnir oscillation, which acts as a fluctuating force for physical processes with even longer time scales, like those that project on the Arctic Oscillation pattern. In this model, the oscillation is represented by strongly correlated noise.


Author(s):  
K.S. Surai ◽  
O.G Obodovskyi ◽  
O.O. Pochaevets

This article gives an estimation of minimum river water flow of the Prut River and the Siret River basins. There are two special periods of low flow between floods. The first one runs from August to November (summer-autumn period) and the second one runs from December to February (winter period). These periods were separated because of different genetics of runoff formation. It was found that 7 days is an optimal period with a stable minimum water flow. Physical and geographical features of the river basin, comparative flow and precipitation graph, scientific works of American scientists became the confirmation for giving preference to 7-days period. All series of observations are homogeneous according to the calculations in the program StokStat. In general, all samples are homogeneous for at least one criterion with 1% level of significance and can be used for further research. The samples of the average 7-day minimum water runoff from the beginning of observations until 2018 became the basis for the construction of maps of the distribution of the minimum water runoff across the study area. The result of this work represents two current maps of the distribution of runoff modules (l*s / km2) of the summer-autumn and winter low water periods. There was conducted the research of the connection between the actual values and the values taken from the maps. The connection between calculated and cartographed minimum modules of water flow showed a close correlation, the approximation coefficients are R2 = 0.9731 and 0.986 for two maps. In this case, the connection line is at an angle close to 45°. We can see a decrease of minimum water flow modules in the direction from west to northeast and a parallel decrease in the density of the distribution of isolines. The altitude has a similar distribution in this area, so there was evaluated a correlation between these two parameters. It is close, the approximation coefficients are R2= 0.5618 and 0.6661 for winter and summer-autumn periods. Consequently, the constructed maps are reliable and can be used in the future to determine the value of minimum flow modules from other parts of the basins for practical and scientific purposes. In particular, this concerns the estimation of the minimum runoff of the river basins, for solving various water management tasks: planning water supply and water intake works, designing and constructing hydrotechnical structures, performing reclamation works, drought assessment.


Author(s):  
Nan Chen ◽  
Andrew J. Majda

AbstractWe assess the predictability limits of the large-scale cloud patterns in the boreal summer intraseasonal variability (BSISO), which are measured by the infrared brightness temperature, a proxy for convective activity. A recent developed nonlinear data analysis technique, nonlinear Laplacian spectrum analysis (NLSA), is applied to the brightness temperature data, defining two spatial modes with high intermittency associated with the BSISO time series. Then a recent developed data-driven physics-constrained low-ordermodeling strategy is applied to these time series. The result is a four dimensional system with two observed BSISO variables and two hidden variables involving correlated multiplicative noise through the nonlinear energyconserving interaction. With the optimal parameters calibrated by information theory, the non-Gaussian fat tailed probability distribution functions (PDFs), the autocorrelations and the power spectrum of the model signals almost perfectly match those of the observed data. An ensemble prediction scheme incorporating an effective on-line data assimilation algorithm for determining the initial ensemble of the hidden variables shows the useful prediction skill in the non-El Niño years is at least 30 days and even reaches 55 days in those years with regular oscillations and the skillful prediction lasts for 18 days in the strong El Niño year (year 1998). Furthermore, the ensemble spread succeeds in indicating the forecast uncertainty. Although the reduced linear model with time-periodic stable-unstable damping is able to capture the non-Gaussian fat tailed PDFs, it is less skillful in forecasting the BSISO in the years with irregular oscillations. The failure of the ensemble spread to include the truth also indicates failure in quantification of the uncertainty. In addition, without the energy-conserving nonlinear interactions, the linear model is sensitive with parameter variations. mcwfnally, the twin experiment with nonlinear stochastic model has comparable skill as the observed data, suggesting the nonlinear stochastic model has significant skill for determining the predictability limits of the large-scale cloud patterns of the BSISO.


2009 ◽  
Vol 10 (5) ◽  
pp. 1285-1297 ◽  
Author(s):  
Sabino Metta ◽  
Jost von Hardenberg ◽  
Luca Ferraris ◽  
Nicola Rebora ◽  
Antonello Provenzale

Abstract A novel rainfall nowcasting method based on the combination of an empirical nonlinear transformation of measured precipitation fields and the stochastic evolution in spectral space of the transformed fields is introduced. The power spectrum and the amplitude distribution of precipitation are kept constant during the forecast, and a Langevin-type model is used to evolve the Fourier phases. The application of the method to a study case is illustrated, and it is shown that, with this procedure, a forecast skill can be obtained that is superior to those provided by Eulerian or Lagrangian persistence for a lead time of up to two hours.


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