Non-linear modelling of the rainfall-runoff transformation

Kachroo, R.K.; Natale, L.

doi:10.1016/0022-1694(92)90095-d

Cited by 47 publications

(34 citation statements)

References 2 publications

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“…(3) is often verified to be approximately equal to the long-term runoff coefficient for the basin being considered, as estimated by Eq. (7) with T set as the calibration period instead of the infinite (Kachroo and Natale, 1992). This merely reflects the linearity and time invariance assumed and inherent to the formulation of the SLM.…”

Section: Simple Linear Rainfall-runoff Model (Slm)mentioning

confidence: 99%

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

Vinagre¹,

Blanco²,

Mesquita³

2011

Journal of Hydrology and Hydromechanics

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The objective of this paper is to simulate flow frequency distribution curves for Amazon catchments with the aim of scaling power generation from small hydroelectric power plants. Thus, a simple nonlinear rainfall-runoff model was developed with sigmoid-variable gain factor due to the moisture status of the catchment, which depends on infiltration, and is considered a factor responsible for the nonlinearity of the rainfall-runoff process. Data for a catchment in the Amazon was used to calibrate and validate the model. The performance criteria adopted were the Nash-Sutcliffe coefficient (R²), the RMS, the Q 95% frequency flow percentage error, and the mean percentage errors ranging from Q 5% to Q 95%. . Calibration and validation showed that the model satisfactorily simulates the flow frequency distribution curves. In order to find the shortest period of rainfall-runoff data, which is required for applying the model, a sensitivity analysis was performed whereby rainfall and runoff data was successively reduced by 1 year until a 1.5-year model application minimum period was found. This corresponds to one hydrological year plus the 6-month long "memory". This analysis evaluates field work in the ungauged sites of the region. Cieľom tohto príspevku je simulácia čiar rozdelenia prietokov pre povodia rieky Amazonka pre potreby hodnotenia premeny energie v malých hydroelektrárňach. Preto bol vyvinutý jednoduchý nelineárny zrážko-odtokový model so sigmoidálne sa meniacim zdrojovým faktorom v závislosti od obsahu vody v povodí, ktorý závisí od infiltrácie a je považovaný za faktor, spôsobujúci nelinearitu zrážkoodtokových procesov. Pre kalibráciu a validizáciu modelu boli použité údaje z povodí rieky Amazonka. Použili sme tieto hodnotiace kritériá: Nashov-Sutcliffov koeficient (R²), RMS, Q 95% , chyba určenia odtoku v percentách, a priemerná percentuálna chyba v rozsahu od Q 5% do Q 95% . Kalibrácia a validizácia ukázala, že model simuluje čiary rozdelenia prietokov uspokojivo. Aby bolo možné nájsť najkratšie obdobie pre nájdenia závislosti zrážky -odtok, ktorá je potrebná pre aplikáciu v modeli, použili sme citlivostnú analýzu tak, že údaje zrážky -odtok boli postupne redukované o jeden rok, až kým nebolo nájdené minimálne obdobie pre aplikáciu vzťahu zrážky -odtok 1,5 roka. Toto obdobie zodpovedá jednému hydrologickému roku, plus 6 mesiacov dlhá "pamäť". Touto analýzou boli vyhodnotené výsledky terénnych meraní v oblastiach, kde neboli k dispozícii merania odtoku.KĽÚČOVÉ SLOVÁ: hydrologické modelovanie, čiara rozdelenia prietokov, malé povodia riekyAmazonka.

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Section: Simple Linear Rainfall-runoff Model (Slm)mentioning

confidence: 99%

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

Vinagre¹,

Blanco²,

Mesquita³

2011

Journal of Hydrology and Hydromechanics

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“…Most traditionally, these models have imposed a rigid functional structure on the input -output transformation. For example, several black-box-type rainfall -runoff models relying on an a priori definition of the functional relationship between rainfall (and other input variables) and discharge have been proposed in the literature (Todini & Wallis 1977;Nash & Barsi 1983;Kachroo & Natale 1992). …”

Section: Introductionmentioning

confidence: 99%

Review of the application of fuzzy inference systems in river flow forecasting

Jacquin

Shamseldin

2009

Journal of Hydroinformatics

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi-Sugeno-Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

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“…WMO, 1975;Nëmec, 1984;WMO, 1992;ASCE, 1993;Legates & McCabe, 1999;Beran, 1999), corresponding to different objectives and different perspectives, the Nash-Sutcliffe index R' probably being the most widely used and perhaps the most important global index for assessing the flood forecasting efficiency. As Kachroo & Natale (1992), Legates & McCabe (1999), and others have reported, it is a rather crude index, being oversensitive to extreme values, because of the squared differences in the definition, while being insensitive to additive and proportional differences between model predictions and observations. This feature will lead to the increasing influence of large floods on the calibrated parameter values and thereby enhance the forecast accuracy of the larger floods.…”

Section: Calculation Of Simulation Efficiencymentioning

confidence: 99%

“…For each such band, a separate standard AR model is used in fitting and forecasting the simulation errors. This concept is analogous to that of identifying separate response functions, as in the CLS-TS/Multilinear rainfall-runoff model of Kachroo & Natale (1992), appropriate to selected bands of streamflow magnitude.…”

Section: Introductionmentioning

confidence: 99%

Comparison of four updating models for real-time river flow forecasting

Xiong¹,

O’Connor²

2002

Hydrological Sciences Journal

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Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) updating model. A fuzzy autoregressive-threshold (FU-AR-TS) updating model is proposed as the third form of model, the fourth and final error-forecast updating model applied being the artificial neural network (ANN) model. In the application of these four updating models, the lumped soil moisture accounting and routing (SMAR) conceptual model has been selected to simulate the observed discharge series on 11 selected test catchments. As expected, it is found that all of these four updating models are very successful in improving the flow forecast accuracy, when operating in real-time forecasting mode. A less expected, but nonetheless welcome, result is that the three updating models having the most parameters, i.e. AR-TS, FU-AR-TS, and ANN, do not show any considerable advantages in improving the real-time flow forecast efficiency over that of the simple standard AR model. Thus it is recommended that, in the context of real-time river flow forecasting based on error-forecast updating, modellers should continue to use the AR model.

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Non-linear modelling of the rainfall-runoff transformation

Cited by 47 publications

References 2 publications

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

Review of the application of fuzzy inference systems in river flow forecasting

Comparison of four updating models for real-time river flow forecasting

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