Quantifying and modelling the ENSO phenomenon and extreme discharge events relation in the La Plata Basin

Meis, Melanie; Llano, María Paula; Rodríguez, Daniela

doi:10.1080/02626667.2020.1843655

Cited by 6 publications

(11 citation statements)

References 29 publications

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“…Accurate estimation of the temporal distribution of rainfall and its trends are crucial input parameters for securing sustainable management of irrigation system. Since, the total rainfall received in a given period at a location is highly variable and often considered as a major source of uncertainty ( [35]; [19]; [36]; [37]; [22]. The analysis of variations of rainfall specially, in arid and semi-arid regions is very crucial where rainfall is scarce and highly variable from one year to another [38].…”

Section: Rainfall Data Collection and Analysismentioning

confidence: 99%

Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

Kerebih

Keshari

2021

Preprint

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In this study, the land and water resources allocation model was developed to determine optimal cropping patterns and water resources allocations at different rainfall probability exceedance levels (PEs) to ensure maximum agricultural return in the Hormat-Golina valley irrigation command area, Ethiopia. To account the uncertainty of rainfall variability, the monthly dependable rainfall was estimated at three levels of reliability (20, 50 and 80% PEs) which are representing wet, normal and dry seasons based on regional experience. The irrigation water demand which was used as an input to the optimization model was estimated at each level of reliability by using CROPWAT model. The net annual returns of optimal cropping patterns were estimated as 181, 179 and 175 million Ethiopia Birr at 20 %, 50 % and 80 % PEs, respectively. The result of the optimal cropping pattern indicates that, the net annual return of the command area was increased to 45.75%, 45.84% and 47.01% than the Government targeted at 20%, 50% and 80% PEs, respectively. The findings reveal that the optimal land and water resources allocation model is very useful to the planners and decision makers to maximize the agricultural return particularly in areas where land and water resources are limited.

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Section: Rainfall Data Collection and Analysismentioning

confidence: 99%

Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

Kerebih

Keshari

2021

Preprint

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“…Different studies (Meis and Llano 2017;Cai et al, 2020, Thielen D, et al, 2020, Cerón et al2020 have documented the connection between extreme events in the La Plata Basin and ENSO (El Niño Southern Oscillation). Furthermore, in Meis et al (2020) the authors built a statistical model to quantify the impact from ENSO's behavior in the seasonal discharge from the Paraná river. These articles indicate that ENSO climatic oscillation may be a relevant variable in hydrological models, particularly for cases when extreme values of streamflow happen.…”

Section: Introductionmentioning

confidence: 99%

“…However, none of them quantify if any improvement can be obtained by using ENSO-related variables for prediction of streamflow series. Instead, the approach taken in Meis et al (2020) is modeling the joint distribution for the streamflow and ENSO index pair. This work can be seen as a natural continuation of it, as rather than directly adding index values to the models, we build exogenous variables using those joint distributions.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we follow the same idea of incorporating climatic indices to discharge modeling, but we propose a more complex two-step approach. First we infer what is the expected streamflow given a value for the climatic index using copula methods to model the joint distribution of the two variables (Meis et al, 2020). Then we use the expected streamflow variable obtained in the first step as a regressor variable in a SARIMAX model for streamflow.…”

Section: Introductionmentioning

confidence: 99%

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A statistical tool for a hydrometeorological forecast in the lower La Plata Basin

Meis

Llano

Rodríguez

2022

International Journal of River Basin Management

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Extreme discharge events in the La Plata Basin need to be prevented. Simple approaches to the forecast problem such as SARIMA models usually predict average values correctly but fail to anticipate extreme events. As an approach to this problem, we used copula methods to model the distribution of the NIÑO 3.4 index and river streamflow pair. We used this to build a 6-months forecast for streamflow 95% percentile using observed index values. We added this forecast as an exogenous variable in a SARIMAX model to predict discharge. Given that NIÑO events are usually correlated with extreme discharge events, we expected this model to improve the SARIMA model in predicting extreme events.When comparing both models, we effectively found that that SARIMAX model is better than a SARIMA model both for six and twelve-month discharge forecasts in periods when an El Niño event occurs, while it retains the same performance level when evaluated on all the span of the time series. This model emerges as a lightweight and easily implementable option for decision makers to anticipate extreme events and reduce the negative impacts that they generate.

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“…Streamflow in rivers is of interest in many fields of research and applications such as climate research (e.g., Meis et al, 2021), hydroelectric power generation (e.g., Popescu et al, 2014), and civil engineering design (e.g., Wang et al, 2015). Since direct methods for measuring discharge are expensive and time consuming in most cases then usually indirect methods are applied.…”

Section: Introductionmentioning

confidence: 99%

Generalization of the power‐law rating curve using hydrodynamic theory and Bayesian hierarchical modeling

Hrafnkelsson

Sigurdarson²,

Rögnvaldsson

et al. 2021

Environmetrics

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The power-law rating curve has been used extensively in hydraulic practice and hydrology. It is given by Q(h) = a(h − c) b , where Q is discharge, h is water elevation, a, b, and c are unknown parameters. We propose a novel extension of the power-law rating curve, referred to as the generalized power-law rating curve. It is constructed by linking the physics of open channel flow to a model of the form h) . The function f (h) is referred to as the power-law exponent and it depends on the water elevation. The proposed model and the power-law model are fitted within the framework of Bayesian hierarchical models. By exploring the properties of the proposed rating curve and its power-law exponent, we find that cross-sectional shapes that are likely to be found in nature are such that the power-law exponent f (h) will usually be in the interval [1.0, 2.67].This fact is utilized for the construction of prior densities for the model parameters. An efficient Markov chain Monte Carlo sampling scheme, that utilizes the lognormal distributional assumption at the data level and Gaussian assumption at the latent level, is proposed for the two models. The two statistical models were applied to four datasets. In the case of three datasets the generalized power-law rating curve gave a better fit than the power-law rating curve while in the fourth case the two models fitted equally well and the generalized power-law rating curve mimicked the power-law rating curve. We developed an R package, bdrc, for fitting the power-law and generalized power-law rating curve models. It is available on CRAN.

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Quantifying and modelling the ENSO phenomenon and extreme discharge events relation in the La Plata Basin

Cited by 6 publications

References 29 publications

Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

Optimal Land And Water Allocation Model For The Maximization of Net Agricultural Return In Drought Prone Regions

A statistical tool for a hydrometeorological forecast in the lower La Plata Basin

Generalization of the power‐law rating curve using hydrodynamic theory and Bayesian hierarchical modeling

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