Condensed disaggregation procedures and conservation corrections for stochastic hydrology

Grygier, Jan C.; Stedinger, Jery R.

doi:10.1029/wr024i010p01574

Cited by 72 publications

(37 citation statements)

References 16 publications

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“…the requirement that disaggregate variables should add up to the aggregate quantity) has also been an issue in these models, though a few studies have effectively handled this problem in some ways (e.g. Bras and Rodriguez-Iturbe, 1985;Grygier and Stedinger, 1988). An important aspect that has to be recognized from the above models is that they present a mathematical framework where a joint distribution of disaggregate and aggregate variables is specified.…”

Section: Introductionmentioning

confidence: 99%

“…The past few decades have witnessed numerous studies addressing the streamflow disaggregation problem and, consequently, a large number of mathematical models (e.g. Harms and Campbell, 1967;Valencia and Schaake, 1972;Salas et al, 1980;Stedinger and Correspondence to: B. Sivakumar (sbellie@ucdavis.edu) Vogel, 1984;Bras and Rodriguez-Iturbe, 1985;Grygier and Stedinger, 1988;Lin, 1990;Santos and Salas, 1992;Maheepala and Perera, 1996). The essence of such models is to develop a staging framework (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Streamflow disaggregation: a nonlinear deterministic approach

Sivakumar

Wallender

Puente

et al. 2004

Nonlin. Processes Geophys.

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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 multidimensional 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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Streamflow disaggregation: a nonlinear deterministic approach

Sivakumar

Wallender

Puente

et al. 2004

Nonlin. Processes Geophys.

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“…Some distortion of the specified distribution of the annual flows results. This small distortion can be ignored, or each year's seasonal flows can be scaled so that their sum equals the specified value of the annual flow (Grygier and Stedinger 1988). The latter approach eliminates the distortion in the distribution of the generated annual flows by distorting the distribution of the generated seasonal flows.…”

Section: ð6:181þmentioning

confidence: 99%

“…As the number of sites included in the disaggregation increases, the size of S xx and B increases rapidly. Very quickly the model can become over parameterized, and there will be insufficient data to estimate all parameters (Grygier and Stedinger 1988).…”

Section: ð6:181þmentioning

confidence: 99%

“…They represent a very flexible modeling framework for dealing with different time or spatial scales. Annual flows for the several sites in question or the aggregate total annual flow at several sites can be the input to the model (Grygier and Stedinger 1988). These must be generated by another model, such as those discussed in the previous sections.…”

Section: Disaggregation Modelmentioning

confidence: 99%

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An Introduction to Probability, Statistics, and Uncertainty

Loucks

Beek

2017

Water Resource Systems Planning and Management

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Stochastic Simulation of Hydrosystems

Koutsoyiannis

2004

Water Encyclopedia

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Simulation is defined as a technique for imitating the evolution of a real system by studying a model of the system. The model is an abstraction, a simplified and convenient mathematical representation of the actual system, typically coded and run as a computer program. If the model has a stochastic element, then we have stochastic simulation . The term stochastic simulation sometimes is used synonymously with the Monte‐Carlo method . Stochastic simulation is regarded as mathematical experimentation and is appropriate for complex systems, whose study based on analytical methods is laborious or even impossible. For such systems, stochastic simulation provides an easy means to explore their behavior by answering specific ‘what if’ questions. Moreover, stochastic simulation can be viewed as a numerical method for solving mathematical problems in several fields such as statistical inference, optimization, integration, and even equation solving. Under certain conditions, stochastic simulation is more powerful than other more common numerical methods (e.g., numerical integration of high‐dimensional differential equations). Due to their complexity, hydrosystems, including water resource systems, flood management systems, and hydropower systems, are frequently studied using stochastic simulation. A generalized solution procedure for hydrosystems problems, including systems identification, modeling and forecasting, hydrologic design, water resources management, and flood management, is dicussed. Emphasis is given on the stochastic representation of hydrologic processes, which have a dominant role in hydrosystems. Peculiarities of hydrologic and other geophysical processes (seasonality, long‐term persistence, intermittency, skewness, spatial variability) gave rise to substantial research that resulted in numerous stochastic tools appropriate for applications in hydrosystems. Four examples of such tools are discussed: (1) the multivariate periodic autoregressive model of order 1 [PAR(1)], which reproduces seasonality and skewness but not long‐term persistence; (2) a generalized multivariate stationary model that reproduces all kinds of persistence and simultaneously skewness but not seasonality; (3) a combination of the previous two cases in a multivariate disaggregation framework that can respect almost all peculiarities except intermittency; and (4) the Bartlett‐Lewis process that is appropriate for modeling rainfall and emphasizes its intermittent character on a fine time scale.

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Condensed disaggregation procedures and conservation corrections for stochastic hydrology

Cited by 72 publications

References 16 publications

Streamflow disaggregation: a nonlinear deterministic approach

Streamflow disaggregation: a nonlinear deterministic approach

An Introduction to Probability, Statistics, and Uncertainty

Stochastic Simulation of Hydrosystems

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