Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.     

Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system

This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.     

Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions

Arabian Journal of Geosciences - Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and...     

不同时间序列模型在岩溶山区矿井涌水量预测中的应用

矿井涌水量预测的精度对于煤矿开采安全有着至关重要的作用。文章以老鹰山煤矿为例,分析降雨与矿井涌水量的相关关系,结果表明：同期月及前第1个月降雨量与涌水量相关性具有逐渐减弱的趋势,而与前第2个月至第5个月的相关性有逐渐升高的趋势;基于矿井涌水量及降雨量,建立了单因素季节性时间序列SARIMA模型及多元季节性时间序列SARIMAX模型对矿井涌水量进行预测,预测结果表明：两种模型91.7%的预测值达到B级探明的矿井涌水量,预测精度均较高,SARIMAX模型预测结果的MAPE为18.57%,小于SARIMA模型的25.27%,预测精度更优。     

南洞地下河月径流时间序列的混沌特征及预测

利用基于相空间重构技术、混沌识别与预测理论对1993-2013年南洞地下河月径流时间序列的非线性特征进行了分析,由所获得的延迟时间和最佳嵌入维数实现了月径流时间序列的相空间重构,运用饱和关联维数法和小数据量法计算出南洞地下河月径流时间序列的饱和关联维数和最大Lyapunov指数,并运用Volterra模型对南洞地下河月径流时间序列进行了多步预测研究。研究结果表明,南洞地下河月径流时间序列相空间重构的延迟时间和最佳嵌入维数分别为τ=5、m=8,饱和关联维数D和最大Lyapunov指数λ分别为4.63、0.748 9,从定性和定量的角度证明了南洞地下河月径流时间序列具有弱混沌特征。Volterra自适应滤波模型的预测结果能较好地表征南洞地下河月径流的变化趋势和规律,对18个月内的短期预测精度较高,模拟效果较好。      

PSO-RBFNN模型及其在岩土工程非线性时间序列预测中的应用

岩土工程受力变形演化是一个典型的非线性问题,其演化的高度非线性和复杂性,很难用简单的力学、数学模型描述,但可用粒子群优化径向基神经网络对岩土工程应力、位移非线性时间序列进行动态实时预测。网络径向基层的单元数通过均值聚类法确定后,所有其它参数：中心位置、形状参数、网络权值,均通过粒子群优化算法在全局空间优化确定。工程实例应用表明,该模型预测结果准确、精度高,有良好的应用前景。     

Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone

Present paper endeavors to develop predictive artificial neural network model for forecasting the mean monthly total ozone concentration over Arosa, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with multiple linear regression model. Ultimately, single-hidden-layer model with 8 hidden nodes have been identified as the best predictive model.     

Evaluation of present-day rainfall simulations over West Africa in CORDEX regional climate models

The objective of this study is to evaluate the ability of seven CORDEX regional climate models (RCMs), driven by ERA-Interim reanalysis dataset to simulate the observed rainfall characteristics over West Africa during the period of 1990–2008. The seasonal climatology, annual rainfall cycles, interannual variability, 850 hPa specific humidity, and wind fields of the RCMs outputs were assessed over a number of spatial scales covering three climatically homogenous subregions (Guinea Coast, Savannah, and Sahel) and the entire West Africa domain. The ability of the RCMs to simulate the response to El Nino and La Nina events were further assessed. Results indicate that the RCMs captured the spatial pattern of rainfall and the three distinctive phases of the West African monsoon reasonably. It is worth noting that RCA and CRCM5 failed to distinctively reproduce the monsoon jump while CCLM, HIRHAM, and REMO largely overestimated the amount of the pre- and the post-monsoon rainfall. The analysis also showed significant biases in individual models depending on the subregion and season under consideration. These biases appear to be linked to the model’s failure to resolve convective processes and topography accurately. The majority of the RCMs used were consistent with the ground observation in capturing the dry (wet) conditions associated with the El Nino (La Nina) events. Statistical analysis conclusively revealed that the RCMs performance varies over the subregions and seasons, implying that no single model is best at all time. In general, REGCM3 was found to be the most outstanding of all the RCMs and is therefore recommended for use in rainfall assessment over West Africa.     

Detecting hydro-climatic change using spatiotemporal analysis of rainfall time series in Western Algeria

The knowledge of the climatic behavior especially that one of semi-arid regions is required to optimize the management of water resources. Here climate variability is directly related to water resources that are of a high socio-economic and environmental significance. This work deals mainly with a statistical analysis of the precipitation regime to assess its spatial distribution and temporal variation in north-western Algeria. For this, a time series and a principal component analysis are performed on rainfall series representing annual precipitations of twenty-one meteorological stations for the period 1914 to 2004, the most complete and longest of West Algeria, in order to detect patterns and trends in the region. A spectral analysis of the time series revealed the existence of a period of roughly 30 years for all stations. Furthermore, the trend of a wide part of the obtained spectra suggests the existence of another period longer than the samples size.     

Comparison of two intelligent models to estimate the instantaneous global solar radiation in semi-arid climate conditions: Application in Iran

Solar radiation incident on the earth’s surface is a fundamental input for many aspects of climatology, hydrology, biology, and architecture. In addition, it is an important parameter in solar energy applications. Due to the high cost of the measuring instruments of solar radiation, many researchers have suggested different empirical methods to estimate this essential parameter. In this study, with the help of fuzzy systems and neural networks, two models have been designed to estimate the instantaneous global solar radiation in Rafsanjan city which has a typical climatic conditions of semi-arid region of middle eastern countries. In fuzzy and neural network model, the inputs are the number of the given day in the year, time, ambient temperature and cloudiness, The comparison between the results of the models and the measurements, shows that the estimated global radiation is similar to the measurement; for fuzzy model, statistical indicators RMSE, MBE and t-test are 103.4367 \((\hbox {w/m}^{2})\), 4.1169 \((\hbox {w/m}^{2})\) and 9.1318, respectively and for ANN, they are 85.46 \((\hbox {w/m}^{2})\), 3.08 \((\hbox {w/m}^{2})\) and 5.41, respectively. As the results indicate, both models are able to estimate the amount of radiation well, while the neural network has a higher accuracy. The output of the modes for six other cities of Iran, with similar climate conditions, also proves the ability of the proposed models.     

深基坑降水中不同的地质模型有限元应用研究

分析水-土耦合场相互作用时,将岩土视作弹塑性材料。在非线性本构关系下,考虑渗透作用,得出岩土体有限元方程,建立了基坑降水的三维水-土耦合的数学模型。对基坑降水中不同的地质模型潜水、承压水及有越流补给和实际工程条件井管、过滤管、止水帷幕分别进行了计算处理,并以武汉国际证券大厦降水工程为实例,对其降水过程进行了三维有限元计算。应用三维数值模拟方法,能很好地模拟基坑防渗帷幕、非均质和各向异性、承压-无压等一些解析法难以处理的实际工程条件和地质模型。     

基于小波的时序改进法在深基坑监测中的应用

结合润扬长江公路大桥南汊悬索桥南锚碇采用排桩冻结法新技术的工程实际,对深基坑支撑轴力监测数据提出了基于小波技术的时间序列改进法,将时间序列分析的多步预报功能与小波对信号精加工的功能相结合,研究结果表明,采用小波改进时间序列分析方法建立的时间序列动态预测模型,其预测精度大大提高,为基坑的安全施工提供了保障,为基坑的信息化施工提供了依据。     

流域降雨径流时间序列的混沌识别及其预测研究进展

混沌和随机在本质上是两种不同的特征,对这两种特征的描述方法各不相同,确定流域降雨径流时间序列的混沌性和随机性是对其进行模拟和预测的重要基础。近10多年来,许多学者相继开展了流域降雨径流时间序列的混沌识别及其预测研究。着重回顾其中最为重要的相空间重构、混沌识别和混沌预测方法,对将混沌理论应用于降雨径流时间序列的限制条件(序列的数据量大小和数据噪声)也进行了探讨。     

喀斯特流域径流量时序演变特征分析——以贵州省为例

以贵阳水文站近45年年径流量序列为例,利用Mo r let 小波变换对喀斯特流域径流量时序演变特性进行了分析预测。结果表明: ( 1)贵阳水文站年径流主要存在4a和25a 两个时间尺度的周期变化,在这两种时间尺度下,径流演变呈现出较明显的丰、枯交替振荡。( 2) 2000年以后的10a左右,年径流量有由丰转为枯、逐步下降的趋势。( 3)年平均径流量的丰、枯周期变化主要受年平均降水量的影响;人类活动对喀斯特流域年径流量有增加和减小两方面的影响,总体上会使径流量略有增加。      

Comparative study of different machine learning models in landslide susceptibility assessment:A case study of Conghua District,Guangzhou,China

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely ...     

Responses of typical grasslands in a semi-arid basin on the Qinghai-Tibetan Plateau to climate change and disturbances

Alpine grassland ecosystems on the Qinghai-Tibetan Plateau (QTP) are vulnerable to climate change and anthropogenic disturbances, which may have significant effects on the QTP’s carbon budgets. In this study, vegetation and soil characteristics were compared among alpine grassland ecosystems in a semi-arid basin on the northeast ridge of the QTP (1) among alpine swamp meadow, meadow, steppe meadow and steppe soils, which represent the direction of succession under climate-warming conditions, and (2) among alpine, degraded and tilled meadow soils to investigate the effects of human disturbance. The results showed that (1) if the alpine grassland ecosystems succeed in this direction, climate warming will cause a loss of carbon, and (2) tilling activity also results in carbon loss. Therefore, these results indicate that anthropogenic disturbance regimes that change more rapidly than climate may exert a more profound influence on carbon dynamics and balance. However, the plots that were set in the present study represented only part of the basin due to road accessibility. Relationships between leaf area index (biomass and soil carbon) and fractional vegetation cover × vegetation height from this study are fairly good, which can be applied at regional scale to estimate carbon pools. In combination with detailed information of grassland types, climate and human activities, the effects of climate change and disturbances can be estimated using remote sensing datasets.     

Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.