Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

Detecting Climate Change Trend, Size, and Change Point Date on Annual Maximum Time Series Rainfall Data for Warri, Nigeria

The study focused on the detection of indicators of climate change in 24-hourly annual maximum series (AMS) rainfall data collected for 36 years (1982-2017) for Warri Township, using different statistical methods yielded a statistically insignificant positive mild trend. The IMD and MCIMD downscaled model’s time series data respectively produced MK statistics varying from 1.403 to 1.4729, and 1.403 to 1.463 which were less than the critical Z-value of 1.96. Also, the slope magnitude obtained showed a mild increasing trend in variation from 0.0189 to 0.3713, and 0.0175 to 0.5426, with the rate of change in rainfall intensity at 24 hours duration as 0.4536 and 0.42 mm/hr.year (4.536 and 4.2 mm/decade) for the IMD and the MCIMD time series data, respectively. The trend change point date occurred in the year 2000 from the distribution-free CUSUM test with the trend maintaining a significant and steady increase from 2010 to 2015. Thus, this study established the existence of a trend, which is an indication of a changing climate, and satisfied the condition for rainfall Non-stationary intensity-duration-frequency (NS-IDF) modeling required for infrastructural design for combating flooding events.

Long-Term Rainfall Trends in South West Asia—Saudi Arabia

In this study, rainfall data from 19 stations in Saudi Arabia (SA) for the period 1985-2019 was utilized to investigate interannual, monthly, and seasonal rainfall variations and trends. The magnitudes of these trends were characterized and tested using Mann-Kendall (MK) rank statistics at different significance levels. During this study period, the mean rainfall in SA showed a slight and significant decreasing trend by about 2 mm/35 years. Investigation of seasonal trends of rainfall revealed that Winter and Spring rainfall decreased significantly by 2.7 mm/35 years and 5.4 mm/35 years respectively. Three months showed very slight significant decreasing trends of rainfall. These were the months of February, March and April. Mann-Kendall analyses were carried out to investigate the annual trends of rainfall during three sub-periods, i.e., 1985-1996, 1997-2008, and 2009-2019. The results revealed that while rainfall increased by 5.3 mm/12 years and 7.8 mm/11 years for the first and the third periods respectively, it decreased by about 11 mm/12 years during the second period. While trends of rainfall in Saudi Arabia are affected by large scale circulations and local factors, the effect of extraterrestrial factors, such as solar activity and its consequent effects on the climate may, additionally, play a potential role in affecting the pattern of rainfall in Saudi Arabia.

Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses

Objective:To study the number of leptospirosis cases in relations to the seasonal pattern,and its association with climate factors.Methods:Time series analysis was used to study the time variations in the number of leptospirosis cases.The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. Results:We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest,namely the northern and northeastern region of Thailand,while the temperature played a role in the northeastern region only.The use of multivariate ARIMA(ARIMAX) model showed that factoring in rainfall(with an 8 months lag) yields the best model for the northern region while the model,which factors in rainfall(with a 10 months kg) and temperature(with an 8 months lag) was the best for the northeaslern region.Conclusions:The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions.The models can also be used to predict the next seasonal peak quite accurately.

Application of Time Series Analysis to Annual Rainfall Values in Debre Markos Town, Ethiopia

For many years planning and management of water resources involved modeling and simulation of temporally sequenced and stochastic hydrologic events. Rainfall process is one of such hydrologic events which calls for time series analysis to better understand interesting features contained in it. Many statistics-based methods are available to simulate and predict such a kind of time series. Autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are among those methods. In this study a search was conducted to identify and examine a capable stochastic model for annual rainfall series (over the period 1954-2015) of Debre Markos town, Ethiopia. For the historical series, normality and stationarity tests were conducted to check if the time series was from a normally distributed and stationary process. Shapiro-Wilk (SW), Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) tests were among the normality tests conducted whereas, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests were among the stationarity tests. Based on the test results, logarithmic transformation and first order differencing were performed to bring the original series to a normal and stationary series. Results of model fitting showed that three models namely, AR (2), MA (1) and ARMA (2,1) were capable in describing the annual rainfall series. A diagnostic check was performed on model residuals and ARMA (2,1) was found to be the best model among the candidates. Furthermore, three information criteria: Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC) were used to select the best model. In this regard, too, the least information discrepancy between the underlying process and the fitted model was obtained from ARMA (2,1) model. Hence, this model was considered as a better representative of the annual rainfall values and was used to predict five years ahead values. The mean absolute percentage error (MAPE) of the prediction was found to be less than 10%. Thus, ARMA (2,1) model could be used for forecasting and simulation of annual rainfall for planning, management and design of water resources systems in Debre Markos town.

Modelling of Indian Monsoon Rainfall Series by Univariate Box-Jenkins Type of Models

The time domain approach, i.e. Autoregressive (AR) processes, of time series analysis is applied to the monsoon rainfall series of India and its two major regions, viz. North-West India and Central India. Since the original time series shows no modelable structure due to the presence of high interannual variability, a 3-point running filter is applied before exploring and fitting appropriate stochastic models. Out of several parsimonious models fitted, AR(3) is found to be most suitable. The usefulness of this fitted model is validted on an independent datum of 18 years and some skill has been noted. These models therefore can be used for low skill higher lead time forecasts of monsoon. Further the forecasts produced through such models can be combined with other forecasts to increase the skill of monsoon forecasts.

Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia

Excessive rainfall is one of the triggers for the flooding phenomenon,especially in the tropics with flat or concave areas.Some critical points in the South Tangerang region,which are currently one of the most rapidly developing cities,cannot be ignored from the flooding problem.Floods cause disturbing human activities,loss of life and property,and in turn affect the economic stretch in an area.This paper aimed to predict rainfall by exploring the application of artificial intelligence techniques such as ANFIS(Adaptive Neuro Fuzzy Inference System).The proposed technique combines neural network learning abilities with transparent linguistic representations of fuzzy systems.The ANFIS model with various input structures and membership functions was built,trained,and tested to evaluate the capability of a model.Analyses of six-year rainfall data on a monthly basis in South Tangerang City,Banten found that rainfall prediction based on ANFIS time series is promising where 80%of data testing is well predicted.

Stochastic Characteristics and Modelling of Monthly Rainfall Time Series of Ilorin, Nigeria

The analysis of time series is essential for building mathematical models to generate synthetic hydrologic records, to forecast hydrologic events, to detect intrinsic stochastic characteristics of hydrologic variables as well to fill missing and extend records. To this end, this paper examined the stochastic characteristics of the monthly rainfall series of Ilorin, Nigeria vis-à-vis modelling of same using four modelling schemes. The Decomposition, Square root transformation-deseasonalisation, Composite, and Periodic Autoregressive (T-F) modelling schemes were adopted. Results of basic analysis of the stochastic characteristics revealed that the monthly series does not show any discernible presence of long-term trend, though there is a seeming inter-decadal annual variation. The series exhibits strong seasonality throughout its length, both in the moments and autocorrelation and significantly intermittent. Based on assessment of the respective models, the performance of the different modelling schemes can be expressed in this order: T-F > Composite > Square root transformation-Deseasonalised > Decomposition. Considering the results obtained, modelling of monthly rainfall series in the presence of serial correlation between months should be based on the establishment of conditional probability framework. On the other hand, in view of the inadequacy of these modelling schemes, because of the autoregressive model components in the coupling protocol, nonlinear deterministic methods such as Artificial Neural Network, Wavelet models could be viable complements to the linear stochastic framework.

Statistical Analysis for Assessing Randomness, Shift and Trend in Rainfall Time Series under Climate Variability and Change: Case of Senegal

The main purpose of this study is to assess the climate variability and change through statistical processing tools that able to highlight annual and monthly rainfall behavior between 1970 and 2010 in six strategical raingauges located in northern (Saint-Louis, Bakel), central (Dakar, Kaolack), and southern (Ziguinchor, Tambacounda) part of Senegal. Further, differences in sensitivity of statistical tests are also exhibited by applying several tests rather than a single one to check for one behavior. Dependency of results from statistical tests on studied sequence in time series is also shown comparing results of tests applied on two different periods (1970-2010 and 1960-2010). Therefore, between 1970 and 2010, exploratory data analysis is made to give in a visible manner a first idea on rainfall behavior. Then, Statistical characteristics such as the mean, variance, standard deviation, coefficient of variation, skewness and kurtosis are calculated. Subsequently, statistical tests are applied to all retained time series. Kendall and Spearman rank correlation tests allow verifying whether or not annual rainfall observations are independent. Hubert’s procedures of segmentation, Pettitt, Lee Heghinian and Buishand tests allow checking rainfall homogeneity. Trend is undertaken by first employing the annual and seasonal Mann-Kendall trend test, and in case of significance, magnitude of trend is calculated by Sen’s slope estimator tests. All statistical tests are applied in the period of 1960-2010. Explanatory analysis data indicates upwards trends for records in northern and central and trend free for southern records. Application of multiple tests shows that the Kendall and spearman ranks correlation tests lead to same conclusion. The difference in tests sensitivity was shown by outcomes of homogeneity tests giving different results either in dates of the shift occurrence or in the significance of an eventual shift. A synthesis analysis of results of tests was carried out to conclude about rainfall behaviors. Tests for homogeneity show that southern rainfall is homogeneous, while northern and central ones are not. According to trend test, upwards trends in Northern and central rainfall trend free in southern assumption in exploratory data analysis have been confirmed. The Sen’s slop estimator shows that all retained trend can be assumed to linear type. The same test over the period 1960-2010 shows independence of observations in all raingauges and exhibits neither trends nor breaks. This seems to show a return to a wet period.

Rainfall Forecasting Using Fourier Series

The need for accurate rainfall prediction is readily apparent when considering many benefits in which such information would provide for river control, reservoir operation, forestry interests, flood mitigation, etc.. Due to importance of rainfall in many aspects, studies on rainfall forecast have been conducted since a few decades ago. Although many methods have been introduced, all the researches describe the study as complex because it involves numerous variables and still need to be improved. Nowadays, there are various traditional techniques and mathematical models available, yet, there are no result on which method provide the most reliable estimation. AR （auto-regressive）, ARMA （auto-regressive moving average）, ARIMA （auto-regressive integrated moving average） and ANNs （artificial neural networks） were introduced as a useful and efficient tool for modeling and forecasting. The conventional time series provide reasonable accuracy but suffer from the assumptions of stationary and linearity. The concept of neurons was introduced first which then developed to ANNs with back propagation training algorithm. Although certain ANNs） models are equivalent to time series model, but it is limited to short term forecasting. This Paper presents a mathematical approach for rainfall forecasting for Iran on monthly basic. The model is trained for monthly rainfall forecasting and tested to evaluate the performance of the model. The result Shows reasonably good accuracy for monthly rainfall forecasting.

Using the Markov Chain for the Generation of Monthly Rainfall Series in a Semi-Arid Zone

Numerous methodologies have been developed in the literature for the generation of rain. However, in semi-arid areas where the irregularity of rain is contrasted, the question of the applicability of these models is still relevant. The objective of this article is to propose a development method of stochastic generator of monthly rainfall series. The present work is based on the modeling of the occurrence and the quantity of rain in a separate way. The occurrence is treated in two stages. The first step considers the Markov chain according to the occurrence of annual statements (dry, average and wet). The second step uses the monthly rankings. The amount of rain is calculated based on historical series according to the monthly rank and the annual statement noted. This method is applied to rainfall data recorded at five rainfall stations in semi-arid region of Central Tunisia. The usual and conventional statistical tests of the generated series have shown the validity of this method.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

Relationship between Indian and East Asian Summer Rainfall Variations

The Indian and East Asian summer monsoons are two components of the whole Asian summer monsoon system. Previous studies have indicated in-phase and out-of-phase variations between Indian and East Asian summer rainfall. The present study reviews the current understanding of the connection between Indian and East Asian summer rainfall. The review covers the relationship of northern China, southern Japan, and South Korean summer rainfall with Indian summer rainfall; the atmospheric circulation anomalies connecting Indian and East Asian summer rainfall variations; the long-term change in the connection between Indian and northern China rainfall and the plausible reasons for the change; and the influence of ENSO on the relationship between Indian and East Asian summer rainfall and its change. While much progress has been made about the relationship between Indian and East Asian summer rainfall variations, there are several remaining issues that need investigation. These include the processes involved in the connection between Indian and East Asian summer rainfall, the non-stationarity of the connection and the plausible reasons, the influences of ENSO on the relationship, the performance of climate models in simulating the relationship between Indian and East Asian summer rainfall, and the relationship between Indian and East Asian rainfall intraseasonal fluctuations.

Forecasting Short Time Series with Missing Data by Means of Energy Associated to Series

In this work an algorithm to predict short times series with missing data by means energy associated of series using artificial neural networks (ANN) is presented. In order to give the prediction one step ahead, a comparison between this and previous work that involves a similar approach to test short time series with uncertainties on their data, indicates that a linear smoothing is a well approximation in order to employ a method for uncompleted datasets. Moreover, in function of the long- or short-term stochastic dependence of the short time series considered, the training process modifies the number of patterns and iterations in the topology according to a heuristic law, where the Hurst parameter H is related with the short times series, of which they are considered as a path of the fractional Brownian motion. The results are evaluated on high roughness time series from solutions of the Mackey-Glass Equation (MG) and cumulative monthly historical rainfall data from San Agustin, Cordoba. A comparison with ANN nonlinear filters is shown in order to see a better performance of the outcomes when the information is taken from geographical point observation.

Generation of rainfall data series by using the Markov Chain model in three selected sites in the Kurdistan Region,Iraq

Rainfall forecasting can play a significant role in the planning and management of water resource systems.This study employs a Markov chain model to examine the patterns,distributions and forecast of annual maximum rainfall(AMR)data collected at three selected stations in the Kurdistan Region of Iraq using 32 years of 1990 to 2021 rainfall data.A stochastic process is used to formulate three states(i.e.,decrease-"d";stability-"s";and increase-"i")in a given year for estimating quantitatively the probability of making a transition to any other one of the three states in the following year(s)and in the long run.In addition,the Markov model is also used to forecast the AMR data for the upcoming five years(i.e.,2022-2026).The results indicate that in the upcoming 5 years,the probability of the annual maximum rainfall becoming decreased is 44%,that becoming stable is 16%,and that becoming increased is 40%.Furthermore,it is shown that for the AMR data series,the probabilities will drop slowly from 0.433 to 0.409 in about 11 years,as indi-cated by the average data of the three stations.This study reveals that the Markov model can be used as an appropri-ate tool to forecast future rainfalls in such semi-arid areas as the Kurdistan Region of Iraq.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

均质非饱和边坡降雨入渗解析解及在黄土边坡的应用

降雨诱发的黄土边坡失稳非常普遍。建立黄土边坡渗流场计算模型,基于非饱和土渗流控制方程。采用VG函数和Gardner函数分别描述土-水特征曲线和渗透系数曲线,利用行波约化和级数展开法推导降雨入渗解析解。利用数值反演法将模型试验数据对土-水参数拟合,证明了该解析解的有效性。对比分析试验值与解析解在不同工况下体积含水率分布规律,分析结果表明:边坡试验模型中的浅层测点体积含水率试验值与解析解较为接近,试验值在浸润锋下移的过程中所达到的体积含水率峰值相比解析解较小;边坡试验模型中的深层测点体积含水率试验值与解析解在前期存在一定误差,深层测点体积含水率解析解在初期增长速度相较于试验值较快,主要原因是较深土层浸润锋的滞后性。

利用2015-2019年Sentinel-1数据监测武汉白沙洲岩溶区地表沉降特征

武汉市白沙洲地区覆盖型岩溶分布广,地质条件十分复杂,历史上多次发生岩溶塌陷,地质问题突出,需要利用有效手段监测区域性地面塌陷,及时发现隐患。文章利用时序InSAR技术对覆盖2015年4月至2019年9月升轨Sentinel-1数据集分析,获取了武汉白沙洲地区地面沉降分布情况,最大年平均变形速率达30 mm·a^(−1)。对典型沉降中心的时序变形分析表明,白沙洲地区的岩溶区地面沉降与季节性降雨密切相关,且有一定的滞后。同时,对武汉地铁6号线车辆段的分析发现,日益加剧的人类活动引起严重的地面沉降,因此需密切注意周围建筑与基础设施的安全问题。本文的实验证明了时序InSAR方法在地表变形监测中的有效性,可在岩溶区地质灾害防治发挥重要作用。