Stochastic series lumped rainfall–runoff model for a watershed in Taiwan

Lee, Chi Chun; Tan, Yih–Chi; Chen, Chu Hui; Yeh, Tian Chyi Jim

doi:10.1016/s0022-1694(01)00410-3

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…First, their conceptually simple framework makes it possible to describe heterogeneity when there are limited spatial or temporal details. Second, they provide decision makers with the ability to determine uncertainty associated with predictions [7,[20][21][22][23][24][25][26].…”

Section: Model Typesmentioning

confidence: 99%

Watershed Modeling and its Applications: A State-of-the-Art Review

Daniel¹,

Camp²,

LeBoeuf³

et al. 2011

TOHYDJ

179

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Advances in the understanding of physical, chemical, and biological processes influencing water quality, coupled with improvements in the collection and analysis of hydrologic data, provide opportunities for significant innovations in the manner and level with which watershed-scale processes may be explored and modeled. This paper provides a review of current trends in watershed modeling, including use of stochastic-based methods, distributed versus lumped parameter techniques, influence of data resolution and scalar issues, and the utilization of artificial intelligence (AI) as part of a data-driven approach to assist in watershed modeling efforts. Important findings and observed trends from this work include (i) use of AI techniques artificial neural networks (ANN), fuzzy logic (FL), and genetic algorithms (GA) to improve upon or replace traditional physically-based techniques which tend to be computationally expensive; (ii) limitations in scale-up of hydrological processes for watershed modeling; and (iii) the impacts of data resolution on watershed modeling capabilities. In addition, detailed discussions of individual watershed models and modeling systems with their features, limitations, and example applications are presented to demonstrate the wide variety of systems currently available for watershed management at multiple scales. A summary of these discussions is presented in tabular format for use by water resource managers and decision makers as a screening tool for selecting a watershed model for a specific purpose.

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Section: Model Typesmentioning

confidence: 99%

Watershed Modeling and its Applications: A State-of-the-Art Review

Daniel¹,

Camp²,

LeBoeuf³

et al. 2011

TOHYDJ

179

View full text Add to dashboard Cite

show abstract

“…Recently, different works on this subject have been published. Some of these use an intensive analytical approach to describe the dynamics of changing statistical characteristics of hydrological systems (Fujita & Kudo 1995;Lee et al 2001;Naidenov & Shveikina 2002Dolgonosov & Korchagin 2007, whereas others look for pseudo-stationary solutions to describe the long-term variations and stability of annual runoff probabilistic patterns (ASCE 1993;Khaustov 1999;Frolov 2006). Previous work on numerical solutions to the FPK equation has been done in the field of probabilistic affluence forecasting, all of which uses numerical schemes with one-directional drift (Kovalenko 1993;Shevnina 2001).…”

Section: Introductionmentioning

confidence: 99%

A Fokker–Planck–Kolmogorov equation approach for the monthly affluence forecast of Betania hydropower reservoir

Domínguez

Rivera²

2010

Journal of Hydroinformatics

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This paper presents a finite difference, time-layer-weighted, bidirectional algorithm that solves the Fokker-Planck-Kolmogorov (FPK) equation in order to forecast the probability density curve (PDC) of the monthly affluences to the Betania hydropower reservoir in the upper part of the Magdalena River in Colombia. First, we introduce a deterministic kernel to describe the basic dynamics of the rainfall-runoff process and show its optimisation using the S/s D performance criterion as a goal function. Second, we introduce noisy parameters into this model, configuring a stochastic differential equation that leads to the corresponding FPK equation. We discuss the set-up of suitable initial and boundary conditions for the FPK equation and the introduction of an appropriate Courant-Friederich-Levi condition for the proposed numerical scheme that uses time-dependent drift and diffusion coefficients. A method is proposed to identify noise intensities.The suitability of the proposed numerical scheme is tested against an analytical solution and the general performance of the stochastic model is analysed using a combination of the Kolmogorov, Pearson and Smirnov statistical criteria.

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“…improved in some regions by including topographic, climatic, and hydrogeologic statistics Lee et al (2001a). developed a stochastic differential equation (SDE) for a lumped rainfall-runoff model and applied it to a watershed that consisted of a number of subwatersheds in series in Taiwan.…”

mentioning

confidence: 99%