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Evaluating the variability in long-term rainfall over India with advanced statistical techniques

Ahmed Ishita Afreen, Salam Roquia, Naikoo Mohd Waseem, Rahman Atiqur, Praveen Bushra, Hoai Pham Ngoc, Pham Quoc Bao, Anh Duong Tran, Tri Doan Quang, Elkhrachy Ismail

Acta Geophysica

2022

Vol. 70, no 2

801--818

Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials.

Predictability of monthly temperature and precipitation using automatic time series forecasting methods

Papacharalampous G., Tyralis H., Koutsoyiannis D.

Acta Geophysica

2018

Vol. 66, no. 4

807--831

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition.

A wavelet-SARIMA-ANN hybrid model for precipitation forecasting

Shafaei M., Adamowski J., Fakheri-Fard M., Dinpashoh Y., Adamowski K.

Journal of Water and Land Development

2016

no. 28

27--36

Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.

Prognozowanie opadów, ze względu na ich znaczenie w gospodarce zasobami wodnymi, szczególnie w zmniejszaniu ryzyka powodzi czy susz, było już przedmiotem wielu badań. Serie miesięcznych opadów mają właściwości nieliniowe i różne skale czasowe, w związku z czym przetestowano różne metody: wavelet, metodę zintegrowanej sezonowej autoregresji z ruchomą średnią (SARIMA) i hybrydową metodę sztucznych sieci neuronowych (ANN) pod kątem ich zdolności do dokładnego przewidywania miesięcznych opadów. Czterdziestoletnią (1970–2009) serię opadów z irańskiej stacji meteorologicznej w Nahavand (34°12’N, 48°22’E) rozłożono na jedną podserię o niskiej częstotliwości i kilka podserii o wysokiej częstotliwości występowania opadów przez transformację falkową. Podserie o niskiej częstotliwości prognozowano za pomocą modelu SARIMA, podczas gdy podserie o wysokiej częstotliwości prognozowano, stosując ANN. Na koniec prognozowane podserie zrekonstruowano celem przewidywania opadów w poszczególnych miesiącach w przyszłości. Porównanie wartości generowanych przez model z danymi z obserwacji wykazało lepszą dokładność prognozowania opadów za pomocą modelu wavelet-SARIMA-ANN niż za pomocą modeli wavelet-ANN i wavelet-SARIMA.