Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.

No abstract

In this study, the aim was to measure changes in the spatio-temporal distribution of a potential drought hazard area and determine the risk status of various meteorological and hydrological droughts by using the kriging, radial basis function (RBF), and inverse distance weighting (IDW) interpolation methods. With that goal, in monthly, three-month, and 12-month time periods drought indices were calculated. Spatio-temporal distributions of the droughts were determined with each drought index for the years in which the most severe droughts were experienced. According to the results, the basin is under risk of meteorological drought due to the occurrence of severe and extreme droughts in most of the area, and especially in the north, during the monthly and three-month time periods. During the 12-month period, it was found that most of the basin is under risk of hydrological drought due to the occurrence of severe and extreme droughts, especially in the southern parts. The most effective interpolation method for the prediction of meteorological and hydrological droughts was determined as kriging according to the results of the cross-validation test. It was concluded that a drought management plan should be made, and early warnings and precautions should be applied in the study area.

Özet Bu çalışmada, Doğu Anadolu Bölgesi'ne düşen aylık ve yıllık toplam yağışların trend analizinin araştırılması hedeflenmiştir. Bu amaçla, Meteoroloji Genel Müdürlüğü'nün 46 adet yağış gözlem istasyonundan alınan, 1960 ile 2013 yılları arasında değişen, verilere Run testi ve Pettitt testi uygulanarak homojenlik analizi yapılmıştır. Homojen olduğu belirlenen istasyonlara Mann-Kendall testi ve Spearman'ın Rho testi uygulanarak trend analizi incelenmiş, Sen'in eğim metodu kullanılarak trendlerin eğimi belirlenmiştir. Aylık toplam yağışların trend analizi değerlendirildiğinde yaz aylarında genellikle yağışların artan yönde eğilimde olduğu, kış aylarında ise azalan yönde eğilimde olduğu görülmüştür. Yazın Haziran ayında bölgede azalan yönde bir eğilim hâkimken, Temmuz ayında yerini artan yönde bir eğilime bırakmaktadır. Kasım ayı olduğunda ise bölgeye düşen aylık toplam yağışlarda tekrardan azalan yönde eğilim hakim olmaktadır.

No abstract