Influence of surface roughness in hydrological response of semiarid catchments

Candela, Angela; Noto, L. V.; Aronica, Giuseppe Tito

doi:10.1016/j.jhydrol.2005.01.023

Cited by 56 publications

(37 citation statements)

References 24 publications

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“…The performance of each parameter set in predicting observed model states is evaluated via a likelihood measure that is used to weight the predictions from the parameter sets (He et al, 2010). The GLUE method transforms the problem of searching for an optimum parameter set into a search for sets of parameter values that would give reliable simulations for a range of model inputs (Candela et al, 2005).…”

Section: Parameter Estimationmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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Dynamic simulation models can increase research effi ciency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, diffi culties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fi tted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from fi ve growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling effi ciency, correlation coeffi cient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. IntroductionDynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al., 1999).Sugarcane (Saccharum spp.) is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al., 1989;Langellier and Martine, 2007;Keating et al., 1999;Thorburn et al., 2005;Inman-Bamber, 1991;Singels et al., 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments. Recent literature contains relatively little work on parameter estimation for crop models (Ahuja and Ma, 2011; Makowski et al., 2006). Makowski et al. (2006) point out the importance of raising the quality of calibration in crop models with automatic procedures for parameter adjustment. This would help ensure that the data are always used appropriately and in the same way for parameter estimation (Wallach et al., 2001), but such procedures are not available for direct use with existing sugarcane models.A new sugarcane model was developed ...

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Section: Parameter Estimationmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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“…GLUE results in a range of parameter sets which all lead to acceptable model runs rather than only one "optimal" calibrated parameter set (Candela et al, 2005). This behaviour is known as "equifinality".…”

Section: T Houska Et Al: Monte Carlo-based Calibration and Uncertaimentioning

confidence: 99%

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

et al. 2014

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Abstract.Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures -for example, by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow for a more detailed analysis of the dynamic behaviour of the soil-plant interface.We coupled two of such high-process-oriented independent models and calibrated both models simultaneously. The catchment modelling framework (CMF) simulated soil hydrology based on the Richards equation and the van Genuchten-Mualem model of the soil hydraulic properties. CMF was coupled with the plant growth modelling framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation.The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 × 10 6 model runs randomly drawn from a uniform distribution.The model was applied to three sites with different management in Müncheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matter of roots, storages, stems and leaves. The shape parameter of the retention curve n was highly constrained, whereas other parameters of the retention curve showed a large equifinality. We attribute this slightly poorer model performance to missing leaf senescence, which is currently not implemented in PMF. The most constrained parameters for the plant growth model were the radiation-use efficiency and the base temperature. Cross validation helped to identify deficits in the model structure, pointing out the need for including agricultural management options in the coupled model.

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“…Ayvaz [13] and Ding, Jia [14] ) and/or for different riverbed materials (e.g. Candela, Noto [15] ), as even the presence of biological soil crusts can affect the surface roughness, runoff and erodibility of the channel. [16] From the aforementioned antecedents, it is clear that soft computing is ideally suited to solve, or overcome the difficulties of engineering sciences, where progress still depends on the advances in theoretical, experimental and computational hydraulics.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

Can we obtain viable alternatives to Manning’s equation using genetic programming?

Gaitán

Balaji

Moore

2016

AIR

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Applied water research, like the one derived from open-channel hydraulics, traditionally links empirical formulas to observational data; for example Manning's formula for open channel flow driven by gravity relates the discharge (Q), cross-sectional average velocity (V ), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel's surface needed in the location of interest. Here we use Genetic Programming (GP), a machine learning technique inspired by nature's evolutionary rules, to derive empirical relationships based on synthetic datasets of the aforementioned parameters. Specifically, we evaluated if Manning's formula could be retrieved from datasets with: a) 300 pentads of A, n, R, S, and Q (from Manning's equation), b) from datasets containing an uncorrelated variable and the parameters from (a), and c) from a dataset containing the parameters from (b) but using values of Q containing noise. The cross-validated results show success retrieving the functional form from the synthetic data in the first two experiments, and a more complex solution of Q for the third experiment. The results encourage the application of GP on problems where traditional empirical relationships show high biases or are non-parsimonious. The results also show alternative flow equations that might be used in the absence of one or more predictors; however, these equations should be used with caution outside of the training intervals.

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Influence of surface roughness in hydrological response of semiarid catchments

Cited by 56 publications

References 24 publications

Process-based simple model for simulating sugarcane growth and production

Process-based simple model for simulating sugarcane growth and production

Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model

Can we obtain viable alternatives to Manning’s equation using genetic programming?

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