A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

Xia, Jun; O’Connor, Kieran M.; Kachroo, R.K.; Liang, G.C.

doi:10.1016/s0022-1694(97)00013-9

Cited by 48 publications

(35 citation statements)

References 21 publications

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“…Because of its empirical basis, the application to catchments with different characteristics could be inappropriate (Ponce and Hawkins, 1996;Corradini et al, 2000). Other indices have been proposed in the literature (Saxton and Lenz, 1967;Gray, 1970;Caporali et al, 1996;Xia et al, 1997;Heggen, 2001;Descroix et al, 2002;Pan et al, 2003;Brocca et al, 2005) that consider different weights, being greater for the most recent precipitation. The weights depend on one parameter linked to the drop in soil moisture during the interstorm periods, where evapotranspiration and drainage processes are significant.…”

Section: Introductionmentioning

confidence: 99%

On the estimation of antecedent wetness conditions in rainfall–runoff modelling

Brocca¹,

Melone²,

Moramarco³

2007

Hydrological Processes

243

171

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Abstract:The analysis of the physical processes involved in a conceptual model of soil water content balance is addressed with the objective of its application as a component of rainfall-runoff modelling. The model uses routinely measured meteorological variables (rainfall and air temperature) and incorporates a limited number of significant parameters. Its performance in estimating the soil moisture temporal pattern was tested through local measurements of volumetric water content carried out continuously on an experimental plot located in central Italy. The analysis was carried out for different periods in order to test both the representation of infiltration at the short time-scale and drainage and evapotranspiration processes at the long time-scale. A robust conceptual model was identified that incorporated the Green-Ampt approach for infiltration and a gravity-driven approximation for drainage. A sensitivity analysis was performed for the selected model to assess the model robustness and to identify the more significant parameters involved in the principal processes that control the soil moisture temporal pattern.The usefulness of the selected model was tested for the estimation of the initial wetness conditions for rainfall-runoff modelling at the catchment scale. Specifically, the runoff characteristics (runoff depth and peak discharge) were found to be dependent on the pre-event surface soil moisture. Both observed values and those estimated by the model gave good results. On the contrary, with the antecedent wetness conditions furnished by two versions of the antecedent precipitation index (API), large errors were obtained.

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Section: Introductionmentioning

confidence: 99%

On the estimation of antecedent wetness conditions in rainfall–runoff modelling

Brocca¹,

Melone²,

Moramarco³

2007

Hydrological Processes

243

171

View full text Add to dashboard Cite

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“…There have been numerous studies that have examined the factors that affect the temporal variability of runoff (Wigmosta & Burges 1997, Xia et al 1997, and Lohmann et al 1998 are recent examples), but fewer studies have examined the factors that affect the spatial variability of runoff. Thornthwaite (1948) and Budyko (1955) used mean annual precipitation and mean annual potential evapotranspiration to identify moisture regimes on a global basis.…”

Section: Introductionmentioning

confidence: 99%

Explaining spatial variability in mean annual runoff in the conterminous United States

Wolock

McCabe²

1999

Clim. Res.

144

156

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The hydrologic concepts needed in a water-balance model to estimate the spatial variation in mean annual runoff for the 344 climate divisions in the conterminous United States (U.S.) were determined. The concepts that were evaluated were the climatic supply of water (precipitation), climatic demand for water (potential evapotranspiration), seasonality in supply and demand, and soilmoisture-storage capacity. Most (91%) of the spatial variability in mean annual runoff for the climate divisions in the conterminous U.S. was explained by the spatial variability of mean annual precipitation minus mean annual potential evapotranspiration. When soil-moisture-storage capacity and seasonality in supply and demand were added to the water balance, the explained variance in mean annual runoff increased slightly, and the error in estimated mean annual runoff decreased significantly. Adding soilmoisture-storage capacity and seasonality in supply and demand provided the most improvement in areas where seasonal supply and demand are out of phase.

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“…It is found that the NLPM-API model was significantly more efficient than the original linear perturbation model [125]. Franchini,et al,1997, have Compared several genetic algorithm (GA)schemes, GA is connected with a conceptual rainfall-runoff model, is such as to make the GA algorithm relatively inefficient [126].…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

Karmakar¹,

Choubey²,

Mishra³

2016

IJAIS

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To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction in long period. It is also found that Neural Network is significant for spatial interpolation of mean climate variables.

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A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

Cited by 48 publications

References 21 publications

On the estimation of antecedent wetness conditions in rainfall–runoff modelling

On the estimation of antecedent wetness conditions in rainfall–runoff modelling

Explaining spatial variability in mean annual runoff in the conterminous United States

Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

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