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Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

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Estuaries around the world are facing numerous threats, including urbanization, industrialization, resource scarcity, and the impacts of climate change. To increase estuarine resilience, it is crucial to manage these ecosystems to maintain their functionality. Sediment transport resilience is a critical factor that affects the performance objectives of navigation, storm damage reduction, and ecosystem restoration. This paper focuses on an integrated resilient sediment transport risk management (IRSTRIM) approach for estuaries. The framework quantifies resilience indexes such as reaction amplitude, graduality, and recovery rates of “sediment transport” to “river and sea interaction” in the Arvand Estuary, the Persian Gulf. Additionally, three indexes, the tidal asymmetry index (TAI), saltwater intrusion vulnerability index, and infill rate, are developed to aid in resilient sediment management. The quantified indexes successfully incorporated tidal asymmetry, sediment characteristics, bed properties, and flow hydrodynamics. Different resilience and resistance management scenarios are evaluated using a decision support system. Based on the results, tidal barrier application, as a resilience scenario, is the best scenario, and the dredging scenario, as a resistance one, is the worst scenario. The reaction amplitude with a weight of 0.39, and the TAI with a weight of 0.27 are determined as the most effective indexes.