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Timely forecasts of the onset or possible evolution of droughts is an important contribution to 9 mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-10 simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-11 M) to predict droughts. Agro-metrological drought index addressing seasonality and 12 autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. 13 Markov chain is adopted to model drought for joint occurrence of different classes of drought 14 severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain 15 model. The proposed model, which benefits suitability of Markov chain models for modeling 16 droughts, showed improvement results in prediction scores relative to classic Markov chain model 17 not including SST-M information, additionally.
The return period is a probabilistic criterion used to measure and communicate the random occurrence of geophysical events such as floods in risk assessment studies. Since an individual risk may be strongly affected by the degree of dependence amongst all risks, the need for the provision of multivariate design quantiles has gained ground. Consequently, several recent studies have focused on estimation of multi-hazard risk resulted from different hazard types. In this study, multi-hazard return periods are derived for riverine and pluvial floods in city of Monza, Italy, based on different copula dependence structures. It is shown that ignoring statistical dependence among different inter-correlated hazards may cause significant misestimation of risks.
Sequential data assimilation methods such as Kalman filters (KF) help the modeler yield more accurate results, especially when correction approaches (like localization) are used. In this paper, the groundwater model of Najafabad Aquifer (central Iran) is used to predict water table levels. The results of the model are assumed to be true and considered as observation data for the filter. Deterministic Ensemble Kalman Filter (DEnKF, a newly released filter) assimilated observations from true run into the model with 2, 5, and 10 times of calibrated values of hydraulic conductivity. This filter was run with/without covariance localization assuming three length-scale parameters. The results showed that DEnKF with inaccurate model parameter values has greatly improved the outputs of the inaccurate model mainly in first few time steps. Localization of the filter causes a change in secondary rising trend of error and makes it a falling trend. The amount of improvement due to localization, based on length-scale parameters, was varied and showed no obvious relationship to the accuracy of the model parameters.
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