Journal articles on the topic 'Multivariate rainfall analysis'
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Nam, Woo-Sung, Tae-Soon Kim, Ju-Young Shin, and Jun-Haeng Heo. "Regional Rainfall Frequency Analysis by Multivariate Techniques." Journal of Korea Water Resources Association 41, no. 5 (May 25, 2008): 517–25. http://dx.doi.org/10.3741/jkwra.2008.41.5.517.
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Liu, Chenglin, Yuwen Zhou, Jun Sui, and Chuanhao Wu. "Multivariate frequency analysis of urban rainfall characteristics using three-dimensional copulas." Water Science and Technology 2017, no. 1 (March 7, 2018): 206–18. http://dx.doi.org/10.2166/wst.2018.103.
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Abstract Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961–2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.
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Fontanazza, C. M., G. Freni, G. La Loggia, and V. Notaro. "Uncertainty evaluation of design rainfall for urban flood risk analysis." Water Science and Technology 63, no. 11 (June 1, 2011): 2641–50. http://dx.doi.org/10.2166/wst.2011.169.
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A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aims to assess the uncertainty of such an approach and for suggesting improvements in the definition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copula method was applied to rainfall features (total depth, duration and maximum intensity) to generate synthetic rainfalls that are more consistent with historical events. The procedure was applied to a real case study, and the results were compared with those obtained by simulating other typical synthetic rainfall events linked to intensity–duration–frequency (IDF) curves. The copula-based multi-variate analysis is more robust and adapts well to experimental flood locations even if it is more complex and time-consuming. This study demonstrates that statistical correlations amongst rainfall frequency, duration, volume and peak intensity can partially explain the weak reliability of flood-frequency analyses based on synthetic rainfall events.
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Mrad, D., S. Dairi, S. Boukhari, and Y. Djebbar. "Applied multivariate analysis on annual rainfall in the northeast of Algeria." Journal of Water and Climate Change 11, no. 4 (May 15, 2019): 1165–76. http://dx.doi.org/10.2166/wcc.2019.272.
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Abstract In recent times, there has been a growing interest in understanding precipitation variability and its predictability for periods of a few months to several years. Our work consisted of studying climatic changes in the northeastern region of Algeria based on multivariate analysis of the annual rainfall. Variability of annual rainfall was analyzed using principal component analysis (PCA) and non-hierarchical classification (cluster method). For spatial rainfall variability, due to the complexity of the region, we used the method inverse distance weighted cartography modeling. Results indicate PCA represented the accumulated yearly rainfall of correlated fields on an annual scale, only the years 1971, 1985, 1995, and 2002 had a rather high degree of correlation, translating the homogeneity of annual distribution of precipitation. Cluster method demonstrated the certainty of three groups. The first group was characterized by regions of distinguishable climatic types, such as Mediterranean climate. The second group was characterized by the Tellian Atlas, while the third group was characterized by high plateaus. Spatial analysis of average decade rainfall shows that the isohyet curves of 750 mm in the center of the study region are shifting to the south, and that the Mediterranean regime rainfall affects all the northern region.
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Jiang, Xinyu, Lijiao Yang, and Hirokazu Tatano. "Assessing Spatial Flood Risk from Multiple Flood Sources in a Small River Basin: A Method Based on Multivariate Design Rainfall." Water 11, no. 5 (May 17, 2019): 1031. http://dx.doi.org/10.3390/w11051031.
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A key issue in assessing the spatial distribution of flood risk is considering risk information derived from multiple flood sources (river flooding, drainage inundation, etc.) that may affect the risk assessment area. This study proposes a method for assessing spatial flood risk that includes flooding and inundation in small-basin areas through multivariate design rainfall. The concept of critical rainfall duration, determined by the time of concentration of flooding, is used to represent the characteristics of flooding from different sources. A copula method is adopted to capture the correlation of rainfall amounts in different critical rainfall durations to reflect the correlation of potential flooding from multiple flood sources. Rainfalls for different return periods are designed based on the copula multivariate analysis. Using the design rainfalls as input, flood risk is assessed following the rainfall–runoff–inundation–loss estimation procedure. A case study of the Otsu River Basin, Osaka Prefecture, Japan, was conducted to demonstrate the feasibility and advantages of this method. Compared to conventional rainfall design, this method considers the response characteristics of multiple flood sources, and solves the problem of flood risk assessment from multiple flood sources. It can be applied to generate a precise flood risk assessment to support integrated flood risk management.
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Gaitan, S., and J. A. E. ten Veldhuis. "Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks." Proceedings of the International Association of Hydrological Sciences 370 (June 11, 2015): 9–14. http://dx.doi.org/10.5194/piahs-370-9-2015.
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Abstract. Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.
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Araújo, Winicius Santos, Francisco Assis Saviano Souza, José Ivaldo Barbosa de Brito, and Lourivaldo Mota Lima. "Estudo Pluvial no Nordeste do Brasil Utilizando Análise Multivariada (Rain Study in Northeast Brazil Using Multivariate Analysis)." Revista Brasileira de Geografia Física 5, no. 3 (November 5, 2012): 448. http://dx.doi.org/10.26848/rbgf.v5i3.232781.
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O objetivo deste trabalho foi estudar a dinâmica de variabilidade climática espacial e temporal da pluviosidade nos nove estados do Nordeste Brasileiro, utilizando as técnicas multivariadas de Análise de Componentes Principais (ACP) e Análise de Agrupamento (AA). Foram utilizadas médias mensais da precipitação pluvial e de mais 11 índices climáticos pluviais definidos pela OMM (Organização Meteorológica Mundial) obtidas a partir de dados diários de 258 estações meteorológicas e/ou postos pluviométricos, fornecidos pela antiga rede de postos da SUDENE/DCA, referentes a um período de 47 anos (1960-2006). Com base nesses dados, foram aplicadas as técnicas de ACP e AA à média pluvial e aos 11 índices pluviais. Na ACP, nove índices climáticos e a média pluvial foram representados por três componentes principais e estas explicaram mais de 90% da variância original dos dados. Na AA, nove índices apresentaram quatro grupos homogêneos de atuação. Palavras - chave: Componentes principais, agrupamento, índices pluviais. Rain Study in Northeast Brazil Using Multivariate Analysis ABSTRACTThe aim of this work was to study the dynamics of spatial and temporal climatic variability in rainfall in the nine states of Northeast Brazil, using the multivariate techniques of Principal Component Analysis (PCA) and Cluster Analysis (CA). We used monthly averages of rainfall and 11 climate indices over rain defined by WMO (World Meteorological Organization) obtained from daily data from 258 meteorological stations and/or climatic stations, supplied by the former service station network SUDENE/DCA, referring a period of 47 years (1960-2006). Based on these data, we applied the techniques the average PCA and CA rain and 11 rain indices. In ACP, nine climate indices and average rainfall were represented by three principal components and these accounted for more than 90% of the variance of the original data. In AA, nine indices showed four homogeneous groups of activity.Keywords: Principal components; cluster; rain indices.
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Nam, Woosung, Hongjoon Shin, Younghun Jung, Kyungwon Joo, and Jun-Haeng Heo. "Delineation of the climatic rainfall regions of South Korea based on a multivariate analysis and regional rainfall frequency analyses." International Journal of Climatology 35, no. 5 (October 28, 2014): 777–93. http://dx.doi.org/10.1002/joc.4182.
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Elesbon, Abrahão A. A., Demetrius D. da Silva, Gilberto C. Sediyama, Hugo A. S. Guedes, Carlos A. A. S. Ribeiro, and Celso B. de M. Ribeiro. "Multivariate statistical analysis to support the minimum streamflow regionalization." Engenharia Agrícola 35, no. 5 (October 2015): 838–51. http://dx.doi.org/10.1590/1809-4430-eng.agric.v35n5p838-851/2015.
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ABSTRACT This study aimed to develop a methodology based on multivariate statistical analysis of principal components and cluster analysis, in order to identify the most representative variables in studies of minimum streamflow regionalization, and to optimize the identification of the hydrologically homogeneous regions for the Doce river basin. Ten variables were used, referring to the river basin climatic and morphometric characteristics. These variables were individualized for each of the 61 gauging stations. Three dependent variables that are indicative of minimum streamflow (Q7,10, Q90 and Q95). And seven independent variables that concern to climatic and morphometric characteristics of the basin (total annual rainfall – Pa; total semiannual rainfall of the dry and of the rainy season – Pss and Psc; watershed drainage area – Ad; length of the main river – Lp; total length of the rivers – Lt; and average watershed slope – SL). The results of the principal component analysis pointed out that the variable SL was the least representative for the study, and so it was discarded. The most representative independent variables were Ad and Psc. The best divisions of hydrologically homogeneous regions for the three studied flow characteristics were obtained using the Mahalanobis similarity matrix and the complete linkage clustering method. The cluster analysis enabled the identification of four hydrologically homogeneous regions in the Doce river basin.
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Thayakaran, R., and N. I. Ramesh. "Multivariate models for rainfall based on Markov modulated Poisson processes." Hydrology Research 44, no. 4 (January 2, 2013): 631–43. http://dx.doi.org/10.2166/nh.2013.180.
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Point process models for rainfall are constructed generally based on Poisson cluster processes. Most commonly used point process models in the literature were constructed either based on Bartlett–Lewis or Neyman–Scott cluster processes. In this paper, we utilize a class of Cox process models, termed the Markov modulated Poisson process (MMPP), to model rainfall intensity. We use this class of models to analyse rainfall data observed in the form of tip time series from rain gauge tipping buckets in a network of gauges in Somerset, southwest England, recorded by the Hydrological Radar Experiment (HYREX). Univariate and multivariate models are employed to analyse the data recorded at single and multiple sites in the catchment area. As the structure of this proposed class of MMPP models allows us to construct the likelihood function of the observed tip time series, we utilize the maximum likelihood methods in our analysis to make inferences about the rainfall intensity at sub-hourly time scales. The multivariate models are used to analyse rainfall time series jointly at four stations in the region. Properties of the cumulative rainfall in discrete time intervals are studied, and the results of fitting three-state models are presented.
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Santiago, Dimas de Barros, Humberto Alves Barbosa, Washington Luiz Félix Correia Filho, and José Francisco de Oliveira-Júnior. "Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis." Sustainability 14, no. 14 (July 18, 2022): 8758. http://dx.doi.org/10.3390/su14148758.
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This study aimed to evaluate the interaction of environmental variables and Water Use Efficiency (WUE) via multivariate analysis to understand the importance of each variable in the carbon–water balance in MATOPIBA. Principal Component Analysis (PCA) was applied to reduce spatial dimensionality and to identify patterns by using the following data: (i) LST (MOD11A2) and WUE (ratio between GPP-MOD17A2 and ET-MOD16A2), based on MODIS orbital products; (ii) Rainfall based on CHIRPS precipitation product; (iii) slope, roughness, and elevation from the GMTED and SRTM version 4.1 products; and (iv) geographic data, Latitude, and Longitude. All calculations were performed in R version 3.6.3 and Quantum GIS (QGIS) version 3.4.6. Eight variables were initially used. After applying the PCA, only four were suitable: Elevation, LST, Rainfall, and WUE, with values greater than 0.7. A positive correlation (≥0.78) between the variables (Elevation, LST, and Rainfall) and vegetation was identified. According to the KMO test, a series-considered medium was obtained (0.7 < KMO < 0.8), and it was explained by one PC (PC1). PC1 was explained by four variables (Elevation, LST, Rainfall, and WUE), among which WUE (0.8 < KMO < 0.9) was responsible for detailing 65.77% of the total explained variance. Positive scores were found in the states of Maranhão and Tocantins and negative scores in Piauí and Bahia. The positive scores show areas with greater Rainfall, GPP, and ET availability, while the negative scores show areas with greater water demand and LST. It was concluded that variations in variables such as Rainfall, LST, GPP, and ET can influence the local behavior of the carbon–water cycle of the vegetation, impacting the WUE in MATOPIBA.
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Ahmad Basri, Muhamad Afdal, Shazlyn Milleana Shaharudin, Kismiantini, Mou Leong Tan, Sumayyah Aimi Mohd Najib, Nurul Hila Zainuddin, and Sri Andayani. "Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches." ISPRS International Journal of Geo-Information 10, no. 10 (October 14, 2021): 689. http://dx.doi.org/10.3390/ijgi10100689.
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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.
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Ahmad Basri, Muhamad Afdal, Shazlyn Milleana Shaharudin, Kismiantini, Mou Leong Tan, Sumayyah Aimi Mohd Najib, Nurul Hila Zainuddin, and Sri Andayani. "Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches." ISPRS International Journal of Geo-Information 10, no. 10 (October 14, 2021): 689. http://dx.doi.org/10.3390/ijgi10100689.
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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.
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SILVEIRA, L. "Multivariate analysis in hydrology: the factor correspondence analysis method applied to annual rainfall data." Hydrological Sciences Journal 42, no. 2 (April 1997): 215–24. http://dx.doi.org/10.1080/02626669709492021.
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Park, Jang, Byong-Ho Jun, and Suk-Hwan Jang. "A Study on the Regionalization of Point Rainfall by Multivariate Analysis Technique." Journal of Korea Water Resources Association 36, no. 5 (October 1, 2003): 879–92. http://dx.doi.org/10.3741/jkwra.2003.36.5.879.
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Wang, Yang, Chuanzhe Li, Jia Liu, Fuliang Yu, Qingtai Qiu, Jiyang Tian, and Mengjie Zhang. "Multivariate Analysis of Joint Probability of Different Rainfall Frequencies Based on Copulas." Water 9, no. 3 (March 9, 2017): 198. http://dx.doi.org/10.3390/w9030198.
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Brito, Thábata T., José F. Oliveira-Júnior, Gustavo B. Lyra, Givanildo Gois, and Marcelo Zeri. "Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil." Meteorology and Atmospheric Physics 129, no. 5 (October 5, 2016): 469–78. http://dx.doi.org/10.1007/s00703-016-0481-x.
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Carducci, Carla Eloize, Leosane Cristina Bosco, Vanderleia Schoeninger, Fábio Satoshi Higashikawa, Rafael Costa Ferreira, and Joyce Castro Xavier. "Multivariate analysis to characterize flaxseed production environments in Brazil." Semina: Ciências Agrárias 42, no. 6supl2 (October 8, 2021): 3685–706. http://dx.doi.org/10.5433/1679-0359.2021v42n6supl2p3685.
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The environments for flaxseed production and its soil-plant-atmosphere relationship, it is essential for distinguish and adapt them to the soil and crop management to obtain high sustainable yields and food diversification. Our goal was to characterize the main edaphoclimatic conditions for flaxseed production in South-Central, Brazil. The experiments were carried out in two locations representative of the edaphoclimatic conditions of South-Central, Brazil: 1 - Dourados, MS, with an Aw climate and LATOSSOLO VERMELHO Distroférrico (Haplustox) and 2 - Curitibanos, SC, with a Cfb climate and CAMBISSOLO HÚMICO (Haplumbrept), both cultivated with four flaxseed varieties: Aguará and Caburé from Argentina, UFSC (reddish-brown color) and Golden (golden-yellow color) from Brazil, grown under no-tillage system and few resources. Data from weather (air temperature and rainfall), plant growth, soil chemical and physical-hydric attributes, and post-harvest quality of flaxseed were monitored. The data were submitted to Pearson’s correlation matrix (P < 0.05) and multivariate principal component analysis (PCA). PCA segregated edaphoclimatic environments and varieties into four distinct groups. Each edaphoclimatic condition there was specific attributes discriminated by PCA ( > 78%). The lowest plant height ( < 0.85m), shorter cycle length (120-142 days) and high yield (≈1.13 Mg ha-1), especially golden-yellow flaxseed, were found in Dourados. The soil organic carbon and rainfall acted directly in Curitibanos, while charge balance and air temperature responded in Dourados influence flaxseed production. Soil physical and grain attributes were similar between the environments investigated. Both agricultural environments showed feasibility for flaxseed sustainable production in Brazil, it is important to emphasize that these results are pioneers, especially the edaphoclimatic conditions from Dourados.
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Boyard-Micheau, Joseph, Pierre Camberlin, Nathalie Philippon, and Vincent Moron. "Regional-Scale Rainy Season Onset Detection: A New Approach Based on Multivariate Analysis." Journal of Climate 26, no. 22 (October 29, 2013): 8916–28. http://dx.doi.org/10.1175/jcli-d-12-00730.1.
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Abstract In agroclimatology, the rainy season onset and cessation dates are often defined from a combination of several empirical rainfall thresholds. For example, the onset may be the first wet day of N consecutive days receiving at least P millimeters without a dry spell lasting n days and receiving less than p millimeters in the following C days. These thresholds are parameterized empirically in order to fit the requirements of a given crop and to account for local-scale climatic conditions. Such local-scale agroclimatic definition is rigid because each threshold may not be necessarily transposable to other crops and other climate environments. A new approach is developed to define onset/cessation dates and monitor their interannual variability at the regional scale. This new approach is less sensitive to parameterization and local-scale contingencies but still has some significance at the local scale. The approach considers multiple combinations of rainfall thresholds in a principal component analysis so that a robust signal across space and parameters is extracted. The regional-scale onset/cessation date is unequally influenced by input rainfall parameters used for the definition of the local rainy season onset. It appears that P is a crucial parameter to define onset, C plays a significant role at most stations, and N seems to be of marginal influence.
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Yang, Zongji, Liyong Wang, Jianping Qiao, Taro Uchimura, and Lin Wang. "Application and verification of a multivariate real-time early warning method for rainfall-induced landslides: implication for evolution of landslide-generated debris flows." Landslides 17, no. 10 (April 3, 2020): 2409–19. http://dx.doi.org/10.1007/s10346-020-01402-w.
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Abstract Rainfall-induced landslides are a frequent and often catastrophic geological disaster, and the development of accurate early warning systems for such events is a primary challenge in the field of risk reduction. Understanding of the physical mechanisms of rainfall-induced landslides is key for early warning and prediction. In this study, a real-time multivariate early warning method based on hydro-mechanical analysis and a long-term sequence of real-time monitoring data was proposed and verified by applying the method to predict successive debris flow events that occurred in 2017 and 2018 in Yindongzi Gully, which is in Wenchuan earthquake region, China. Specifically, long-term sequence slope stability analysis of the in situ datasets for the landslide deposit as a benchmark was conducted, and a multivariate indicator early warning method that included the rainfall intensity-probability (I-P), saturation (Si), and inclination (Ir) was then proposed. The measurements and analysis in the two early warning scenarios not only verified the reliability and practicality of the multivariate early warning method but also revealed the evolution processes and mechanism of the landslide-generated debris flow in response to rainfall. Thus, these findings provide a new strategy and guideline for accurately producing early warnings of rainfall-induced landslides.
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He, Yaqian, and Eungul Lee. "Empirical Relationships of Sea Surface Temperature and Vegetation Activity with Summer Rainfall Variability over the Sahel*." Earth Interactions 20, no. 6 (February 1, 2016): 1–18. http://dx.doi.org/10.1175/ei-d-15-0028.1.
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Abstract Regional land surface and remote ocean variables have been considered as primary forcings altering the variability of summer rainfall over the Sahel. However, previous studies usually examined the two components separately. In this study, the authors apply statistical methods including correlation, multivariate linear regression, and Granger causality analyses to investigate the relative roles of spring–summer sea surface temperature (SST) and vegetation activity in explaining the Sahel summer rainfall variability from 1982 to 2006. The remotely sensed normalized difference vegetation index (NDVI) is used as an indicator of land surface forcing. This study shows that spring and summer SSTs over the subtropical North Atlantic have significant positive correlations with summer rainfall. The spring and summer NDVIs over the Sahel have significant negative and positive correlations, respectively, with summer rainfall. Based on the multivariate linear regression analysis, the adjusted R2 for the integrated model with both the land and ocean variables is 0.70. It is around 2 times larger than the model with SST alone (adjusted R2 = 0.36). To further investigate the causal relationships of summer rainfall with the SST and NDVI variables selected in the integrated multivariate model, the authors perform a Granger causality test. This study finds that summer NDVI over the Sahel does Granger cause summer rainfall over the Sahel, while the summer SST over the subtropical North Atlantic does not Granger cause the summer rainfall. The results indicate that the regional land surface forcing has a relatively strong contribution to Sahel summer rainfall, compared to the remote ocean forcing, during the recent decades.
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Silva, Jhon Lennon Bezerra da, Geber Barbosa De Albuquerque Moura, Marcos Vinícios Da Silva, Roni Valter De Souza Guedes, Pabrício Marcos Oliveira Lopes, Ênio Farias de França e Silva, Rochele Sheila Vasconcelos, and Anna Hozana Francilino. "Inferência Exploratória de Dados Espaço-Temporal da Precipitação Pluviométrica no Nordeste Brasileiro." Revista Brasileira de Geografia Física 13, no. 5 (July 29, 2020): 2019. http://dx.doi.org/10.26848/rbgf.v13.5.p2019-2036.
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A gestão eficiente dos recursos hídricos no Nordeste brasileiro torna-se fundamental diante do regime hidrológico dos rios intermitentes, dos quais muitos são extremamente críticos. Todavia estes dependem de um regime pluviométrico irregular, tanto em escala de tempo mensal quanto anual. Objetivou-se determinar a variabilidade espaço-temporal da precipitação pluviométrica total anual, averiguando-se, também, as regiões com padrões de precipitação semelhantes por técnicas de análise multivariada (clusters e componentes principais) no Nordeste do Brasil. Foram analisados dados de precipitação pluviométrica total anual, entre os anos de 1995 e 2016, de 37 diferentes estações meteorológicas do INMET, estas situadas nos limites territoriais dos nove estados do Nordeste brasileiro. A análise de clusters verificou a formação de quatro grupos distintos, com padrões semelhantes de precipitação nas regiões dentro dos grupos, conforme também observado na análise de componentes principais. A padronização e/ou variabilidade espaço-temporal da precipitação pluviométrica dos municípios analisados mostrou-se está intimamente associada as condições das estações do ano e anomalias climatológicas, aos fatores de uso e ocupação do solo, condições de altitude e relevo, tais quais favorecem na formação e estabilidade de chuvas menores ou maiores no Nordeste brasileiro. A análise multivariada de cluster e componentes principal identificaram padrões e semelhanças pluviométricas de grupos, nos diferentes estados do Nordeste do Brasil entre os anos de 1995 e 2016. Exploratory Inference of Spatial-Temporal Data of Rainfall in the Brazilian Northeast ABSTRACTThe efficient management of water resources in the Northeast of Brazil is essential in view of the hydrological regime of intermittent rivers, of which many are extremely critical, as they depend on an irregular rainfall regime, both on a monthly and annual time scale. The objective of this study was to determine the spatial and temporal variability of the annual total rainfall, also investigating the regions with similar rainfall patterns by multivariate analysis techniques (clusters and principal components) in Brazilian Northeast. Data from total annual rainfall between the years 1995 and 2016, of 37 different INMET weather stations were analyzed, located within the territorial limit of the nine states of Brazilian Northeast. Cluster analysis verified the formation of four distinct groups, with similar precipitation patterns in the regions within the groups as also observed in the principal component analysis. The pattern and/or spatial-temporal variability of rainfall in the municipalities analyzed was shown to be intimately associated with the conditions of the year and climatic anomalies stations, and the factors of land use and occupation, altitude and relief conditions, such as favoring the formation and stability of minor or major rain in the Brazilian Northeast. Multivariate cluster and principal component analysis identified rainfall patterns and similarities of groups, in the different states of Northeastern Brazil between the years 1995 and 2016.Keywords: multivariate analysis, climate change, semiarid, regional climate patterns.
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Yoo and Cho. "Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events." Water 11, no. 5 (April 29, 2019): 905. http://dx.doi.org/10.3390/w11050905.
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A rainfall event, simplified by a rectangular pulse, is defined by three components: the rainfall duration, the total rainfall depth, and mean rainfall intensity. However, as the mean rainfall intensity can be calculated by the total rainfall depth divided by the rainfall duration, any two components can fully define the rainfall event (i.e., one component must be redundant). The frequency analysis of a rainfall event also considers just two components selected rather arbitrarily out of these three components. However, this study argues that the two components should be selected properly or the result of frequency analysis can be significantly biased. This study fully discusses this selection problem with the annual maximum rainfall events from Seoul, Korea. In fact, this issue is closely related with the multicollinearity in the multivariate regression analysis, which indicates that as interdependency among variables grows the variance of the regression coefficient also increases to result in the low quality of resulting estimate. The findings of this study are summarized as follows: (1) The results of frequency analysis are totally different according to the selected two variables out of three. (2) Among three results, the result considering the total rainfall depth and the mean rainfall intensity is found to be the most reasonable. (3) This result is fully supported by the multicollinearity issue among the correlated variables. The rainfall duration should be excluded in the frequency analysis of a rainfall event as its variance inflation factor is very high.
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Mazzoglio, Paola, Ilaria Butera, Massimiliano Alvioli, and Pierluigi Claps. "The role of morphology in the spatial distribution of short-duration rainfall extremes in Italy." Hydrology and Earth System Sciences 26, no. 6 (March 30, 2022): 1659–72. http://dx.doi.org/10.5194/hess-26-1659-2022.
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Abstract. The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration annual maximum rainfall depths and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2-RED, the Improved Italian-Rainfall Extreme Dataset), we present a systematic study of the relationship between geomorphological forms and the average annual maxima (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of the index rainfall. The inclusion of longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall contributes to the explanation of a larger percentage of the variance, even though this was in different ways for different durations (1 to 24 h). After analyzing the spatial variability of the regression residuals, we repeated the analysis on geomorphological subdivisions of Italy. Comparing the results of the best multivariate regression models with univariate regressions applied to small areas, deriving from morphological subdivisions, we found that “local” rainfall–topography relationships outperformed the country-wide multiple regressions, offered a uniform error spatial distribution and allowed the effect of morphology on rainfall extremes to be better reproduced.
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Gao, Yuqin, Zichen Guo, Dongdong Wang, Zhenxing Zhang, and Yunping Liu. "Multivariate Flood Risk Analysis at a Watershed Scale Considering Climatic Factors." Water 10, no. 12 (December 10, 2018): 1821. http://dx.doi.org/10.3390/w10121821.
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Based on the constructed SWAT model in the Qinhuai River Basin, the hydrological response of flooding under different scenarios of temperature and rainfall change is analyzed. The Copula function is then used to calculate and analyze the multivariate flood risk. The results show that the flood peaks increase with the increase of precipitation and decrease with the increase of temperature. The hydrological response of light floods to temperature changes is stronger than that of medium and heavy floods. Additionally, the temperature drop and the precipitation increase lead to a higher flood risk. The flood risk of flood peaks is more sensitive to changes in precipitation.
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Shiau, Jenq-Tzong, Hsin-Yi Wang, and Chang-Tai Tsai. "Copula-based depth-duration-frequency analysis of typhoons in Taiwan." Hydrology Research 41, no. 5 (June 1, 2010): 414–23. http://dx.doi.org/10.2166/nh.2010.048.
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Typhoons are an inevitable and frequently occurring natural hazard in Taiwan which cause severe economic damage and loss of life. The common practice for flood-mitigation planning and design uses univariate frequency analysis. However, separate univariate analysis cannot reveal the significant relationship among correlated variables. This study therefore employs copulas to construct the joint distribution of rainfall depth and duration for typhoon data. Using copulas to construct a multivariate distribution means that the effects of marginal variables can be separated from that of dependent variables. We derive the depth-duration-frequency (DDF) formula based on using copulas to represent the joint distribution of rainfall depth and duration. Typhoon data recorded at the Kaohsiung Weather Station located in southern Taiwan are used as an example to illustrate the proposed methodology. The marginal distributions for rainfall depth and duration are fitted as the three-parameter gamma and Gumbel distributions, respectively. The Plackett copula is selected to construct the DDF curves. The DDF allows rainfall depth for a specific rainfall duration and return period to be estimated. This DDF formula improves the understanding of complex hydrologic processes and enhances the design safety criterion of hydraulic structures.
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SOUSA, MARCOS MAKEISON MOREIRA DE, HELBA ARAÚJO DE QUEIROZ PALÁCIO, EUNICE MAIA DE ANDRADE, JACQUES CARVALHO RIBEIRO FILHO, and MATHEUS MAGALHÃES SILVA MOURA. "DETERMINANT PLUVIOMETRIC CHARACTERISTICS OF SEDIMENT TRANSPORT IN A CATCHMENT WITH THINNED VEGETATION IN THE TROPICAL SEMIARID." Revista Caatinga 33, no. 3 (September 2020): 785–93. http://dx.doi.org/10.1590/1983-21252020v33n322rc.
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ABSTRACT Knowing determinant factors of erosive process is essential to adopt soil conservationist and loss-mitigation measures. Therefore, the objective of this work was to assess the correlation between rainfall characteristics and sediment transport in the Semiarid region of Brazil. The study was conducted at the Iguatu Experimental Basin in the state of Ceará, Brazil, in a watershed with area of 1.15 ha. The vegetation was thinned by removal of plants with diameters below 10 cm, and the area remained with an arboreous cover of 60%. The following variables were evaluated from 2012 to 2016: rainfall depth (mm), rainfall duration (hours), maximum rainfall intensity in 5, 10, 15, 20, 30, 45, and 60 minutes (mm h-1), mean rainfall intensity (mm h-1), rainfall depth in the previous 5 days (mm), runoff depth (mm), and transported sediment (kg ha-1). The records showed 158 rainfall events, 27 with surface runoff and 24 with sediment transport. The correlations were investigated by multivariate analysis of principal components (PC). The model explained 84% of total variance with four PC-PC1, PC2, PC3, and PC4 were formed, respectively, for disaggregating power of rainfall on soil particles, represented by the rainfall intensities; soil water content; runoff depth and sediment transport; and rainfall duration and interval between rainfalls. The highest factorial weight was found for the maximum intensity in 20 minutes, indicating the need for further hydrological studies focused on this variable at basin scale in areas of the Semiarid region of Brazil subjected to thinning of the vegetation.
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Bowo, Arisya Maulina, Iin Irianingsih, and Budi Nurani Ruchjana. "Canonical Correlation Analysis of Global Climate Elements and Rainfall in the West Java Regions." Desimal: Jurnal Matematika 3, no. 2 (May 28, 2020): 143–54. http://dx.doi.org/10.24042/djm.v3i2.5870.
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Indonesia has a diversity of climate influenced by several global phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Asian-Australian Monsoon. Continuously climate changing indirectly causes a hydrometeorological disaster. The purpose of this study was to analyze the relationship between global climate elements (ENSO, IOD, Asian-Australian Monsoon) with rainfall in the West Java regions (Bogor Regency, Bandung Regency, Sukabumi Regency, Garut Regency, and Kuningan Regency) simultaneously. The selection of the five regions was based on the natural disaster reports of Badan Nasional Penanggulangan Bencana (BNPB). The research method used was a quantitative research method through one of multivariate analysis technique called canonical correlation analysis. The results of this study indicate that there was a simultaneous relationship between global climate elements, with rainfall in the West Java regions by 0.819. The global climate element and rainfall in the West Java regions that most influenced the relationship were Asian-Austalian Monsoon and Kuningan Regency rainfall.
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Abedini, Mandana, Md Azlin Md Said, and Fauziah Ahmad. "Integration of statistical and spatial methods for distributing precipitation in tropical areas." Hydrology Research 44, no. 6 (December 5, 2012): 982–94. http://dx.doi.org/10.2166/nh.2012.159.
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The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta Catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.
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Terassi, Paulo Miguel de Bodas, José Francisco de Oliveira Júnior, Givanildo de Gois, Bruno Serafini Sobral, Emerson Galvani, and Vitor Hugo Rosa Biffi. "Analysis of Daily Rainfall and Spatiotemporal Trends of Extreme Rainfall at Paraná Slope of the Itararé Watershed, Brazil." Revista Brasileira de Meteorologia 35, no. 2 (June 2020): 357–74. http://dx.doi.org/10.1590/0102-7786352025.
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Abstract The knowledge of intensity and frequency of rainfall allows establishing predictive measures to minimize impacts caused by high volume of rainfall totals in a region. Therefore, the objective is to evaluate daily rainfall for Paraná slope of the Itararé watershed (PSIW) and to verify the spatiotemporal trend of intense and extreme daily rainfall. Rainfall data from 14 stations collected from 1976 to 2012 were used with less than 4% of data faults. Multivariate analysis based on cluster analysis technique (CA) was used applying the Euclidean distance for the identification of homogeneous groups, and the quantiles technique to classify daily rainfall. The Mann-Kendall (MK) test was used to identify trends for annual rainfall totals, annual number of rainy days (ANRD) and for the occurrence of intense (R95p) and extreme (R99p) rainfall. The CA technique identified three rainfall groups (HG I, II and III). Given the latitudinal position of the area, rainfall at the southern sector is characterized by its greater similarities with the subtropical climate, whereas in the North sector there is a consistent reduction of rainfall totals in autumn and, especially, during winter months, which are characteristic of the tropical climate. The MK test identified the downward trend of ANRD, with greater significance for the south-centered sectors of the basin. The observed trends for the intense (R95p) and extreme (R99p) daily rainfall show the predominance of reduction for the Southwest and central sector, followed by a significant increase in the Southeast and North sectors of the PSIW.
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Dhanya, M., and A. Chandrasekar. "Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India." Annales Geophysicae 34, no. 2 (February 9, 2016): 187–201. http://dx.doi.org/10.5194/angeo-34-187-2016.
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Abstract. The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.
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Hastuti, Retno Tri, and Lucia Yovita Hendrati. "Spatial Analysis of Dengue Hemorrhagic Fever based on Influencing Factors in Jombang, 2014–2018." Jurnal Berkala Epidemiologi 9, no. 1 (January 29, 2021): 79. http://dx.doi.org/10.20473/jbe.v9i12021.79-87.
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Background: Jombang District is an endemic area of dengue hemorrhagic fever (DHF). Purpose: The aim of this study was to spatially analyze various factors simultaneously (multivariate analysis) in relation to the incidence of DHF in Jombang District during the period 2014–2018. The factors studied were population density, larvae free index, rainfall, coverage of healthy homes, and healthy lifestyle coverage. Methods: The research was conducted as an observational study with an ecology research design. The data were secondary data from the Health Office and Statistic Central Bureau of Jombang District. The population consisted of 21 sub-districts in Jombang District in 2014–2018. The sample used the total population. The data analysis tool used in this study was GeoDa regression Moran's I software. Results: The bivariate analysis showed that there was a correlation between larvae free index (p = 0.04), healthy lifestyle coverage (p = 0.02), rainfall intensity (p = 0.20), population density (p = 0.07), and coverage of healthy houses (p = 0.22) with DHF incidence. According to Moran's I for spatial dependence (multivariate analysis), showed that there was a correlation between all the variables and DHF (p = 0.03). Conclusions: The variables of larvae free index and healthy lifestyle coverage related to the Incidence Rate (IR) of DHF cases. There was no correlation between IR and variable population density, rainfall, or coverage of healthy homes. Various spatial factors are simultaneously related to IR, even though only two variables are shown to be related to IR in the bivariate analysis.
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Jane, Robert, Luis Cadavid, Jayantha Obeysekera, and Thomas Wahl. "Multivariate statistical modelling of the drivers of compound flood events in south Florida." Natural Hazards and Earth System Sciences 20, no. 10 (October 10, 2020): 2681–99. http://dx.doi.org/10.5194/nhess-20-2681-2020.
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Abstract. Miami-Dade County (south-east Florida) is among the most vulnerable regions to sea level rise in the United States, due to a variety of natural and human factors. The co-occurrence of multiple, often statistically dependent flooding drivers – termed compound events – typically exacerbates impacts compared with their isolated occurrence. Ignoring dependencies between the drivers will potentially lead to underestimation of flood risk and under-design of flood defence structures. In Miami-Dade County water control structures were designed assuming full dependence between rainfall and Ocean-side Water Level (O-sWL), a conservative assumption inducing large safety factors. Here, an analysis of the dependence between the principal flooding drivers over a range of lags at three locations across the county is carried out. A two-dimensional analysis of rainfall and O-sWL showed that the magnitude of the conservative assumption in the original design is highly sensitive to the regional sea level rise projection considered. Finally, the vine copula and Heffernan and Tawn (2004) models are shown to outperform five standard higher-dimensional copulas in capturing the dependence between the principal drivers of compound flooding: rainfall, O-sWL, and groundwater level. The work represents a first step towards the development of a new framework capable of capturing dependencies between different flood drivers that could potentially be incorporated into future Flood Protection Level of Service (FPLOS) assessments for coastal water control structures.
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PRASAD, K. "A diagnostic study of flood producing rainstorm of September 1988 over northwest India with the aid of a fine mesh numerical analysis system." MAUSAM 43, no. 1 (December 30, 2021): 71–76. http://dx.doi.org/10.54302/mausam.v43i1.3320.
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A numerical analysis of the synoptic situation leading to devastating floods in Punjab and adjoining states during September 1988 has been carried out. The analysis is done by three dimensional multivariate optimum interpolation (OI) scheme cast on 1° x 1° Lat./Long. Grid. Software has been developed for computation of several derived parameters and linked with the basic flow variable analysis. A diagnostic study of day-to-day rainfall versus the objectively analysed grid point fields of integrated horizontal flux divergence of water vapour is carried out, The study brings out a close spatial correspondence between the area of net moisture flux convergence on the analysis day and the area of heavy rainfall on the following day. The study suggests that the numerical analysis products can be of a good predictive value to a synoptic forecaster In heavy rainfall predictions under difficult and uncertain synoptic situations.
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& Karim, Keya. "MULTIVARIATE MODELS FOR PREDICTING RAINFALL EROSIVITY FROM ANNUAL RAINFALL AND GEOGRAPHIVAL COORDIATES IN A REGION WITH A NON- UNIFORM PLUVIAL REGIME." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 51, no. 5 (October 30, 2020): 1249–61. http://dx.doi.org/10.36103/ijas.v51i5.1133.
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Soil erosion by water is a major land degradation problem because it threatens the farmer’s livelihood and ecosystem's integrity. Rainfall erosivity is one of the major controlling factors inducing this process. One obstacle of estimating the R-factor is the lack of detailed rainfall intensity data worldwide. To overcome the problem of data scarceness for individual analysis of storm events for developing the country with a non-uniform pluvial regime like the upper part of Iraq, multivariate models were derived for estimating annual rainfall erosivity. They were based on annual rainfall data and geographical coordinates of a group of meteorological stations distributed over the study area. A host of statistical indices were selected to evaluate adequately the model's performance. Further, the models were cross-validated using k-fold procedure and unseen data. Subsequently, four linear models were proposed for estimating the annual erosivity for the study area. Good correspondence was found between the measured and predicted values. Among the proposed models, the model with the combination of annual rainfall, latitude and longitude outperformed the remaining proposed ones. After calculating the annual, the ArcMap software ver. 10.2 was applied to map the spatial variability of the R-factor over the study region.
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JHA, T. N., and R. D. RAM. "Study of rainfall departure over catchments of Bihar plains." MAUSAM 61, no. 2 (November 27, 2021): 187–96. http://dx.doi.org/10.54302/mausam.v61i2.800.
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Station wise daily rainfall data of sixty years is used to study rainfall departure and variability in Kosi, Kamala/Bagmati/Adhwara and Gandak/Burhi Gandak catchments during monsoon season. Station and catchment wise rainfall time series have been made to compute rainfall departure and Coefficient of Variation (CV). Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI) and ENSO strength based on percentile analysis are used to ascertain their impact on rainfall distribution in the category as excess, normal, deficient and scanty. Results indicate that the variability is greater over Kosi as compared to the other catchments. Probability of normal rainfall is found 0.75 and there is no possibility of scanty rain over the catchments during El Nino and La Nina year. Similarly probabilities of normal, deficient, excess rainfall are found as 0.67, 0.18 and 0.15 respectively during mixed year. SOI has emerged as principal parameter which modifies the departure during El Nino and La Nina year. MEI along with ENSO strength are more prominent during mixed year particularly to ascertain deficient and excess rain in weak and strong- moderate La Nina respectively .
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Liu, Ping, Yu Wang, Tongze Han, Jiaming Xu, and Qiangnian Li. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model." Sustainability 14, no. 16 (August 10, 2022): 9886. http://dx.doi.org/10.3390/su14169886.
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Regional extreme rainfall events have occurred frequently in China, and subway tunnel construction faces possible threats under extreme weather conditions. Thus, in this study, we used the set pair analysis (SPA) approach to the construction safety evaluation of subway tunnels and developed a construction safety evaluation model under extreme rainfall circumstances. Firstly, based on careful consideration of the complex construction environment of subway tunnels under extreme rainfall weather conditions, a construction safety evaluation system of subway tunnels was developed considering four aspects: rainfall, hydrogeology, construction design, and management. Moreover, the weighting analysis of each index factor was carried out using the improved analytic hierarchy process (IAHP) method, the entropy weight method (EWM), and the linear weighting method. Secondly, considering the uncertainty of subway tunnels’ construction safety evaluation system and the fuzzy nature of evaluation-level classification, a construction safety evaluation system of subway tunnels based on the multivariate linkage number and set pair analysis theory was established. Finally, we applied the model to a subway tunnel construction case. The results show that the evaluation results are consistent with the actual engineering survey results, which verifies the practicality and effectiveness of the model in evaluating subway tunnel safety. We also determined the primary factors and risk development trends that affect the safety of subway tunnel construction under extreme rainfall weather conditions to guide the safety risk management of subway tunnel construction.
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Tushaus, Samantha A., Derek J. Posselt, M. Marcello Miglietta, Richard Rotunno, and Luca Delle Monache. "Bayesian Exploration of Multivariate Orographic Precipitation Sensitivity for Moist Stable and Neutral Flows." Monthly Weather Review 143, no. 11 (October 29, 2015): 4459–75. http://dx.doi.org/10.1175/mwr-d-15-0036.1.
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Abstract Recent idealized studies examined the sensitivity of topographically forced rain and snowfall to changes in mountain geometry and upwind sounding in moist stable and neutral environments. These studies were restricted by necessity to small ensembles of carefully chosen simulations. Research presented here extends earlier studies by utilizing a Bayesian Markov chain Monte Carlo (MCMC) algorithm to create a large ensemble of simulations, all of which produce precipitation concentrated on the upwind slope of an idealized Gaussian bell-shaped mountain. MCMC-based probabilistic analysis yields information about the combinations of sounding and mountain geometry favorable for upslope rain, as well as the sensitivity of orographic precipitation to changes in mountain geometry and upwind sounding. Exploration of the multivariate sensitivity of rainfall to changes in parameters also reveals a nonunique solution: multiple combinations of flow, topography, and environment produce similar surface rainfall amount and distribution. Finally, the results also divulge that the nonunique solutions have different sensitivity profiles, and that changes in observation uncertainty also alter model sensitivity to input parameters.
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Sandoval, S., A. Torres, E. Pawlowsky-Reusing, M. Riechel, and N. Caradot. "The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study." Water Science and Technology 68, no. 12 (October 25, 2013): 2683–90. http://dx.doi.org/10.2166/wst.2013.524.
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The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.
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VYAS, MM, ML PUROHIT, and BL GAJJA. "Crop productivity and environmental attributes in Jodhpur district of western Rajasthan." MAUSAM 36, no. 2 (April 5, 2022): 203–4. http://dx.doi.org/10.54302/mausam.v36i2.1866.
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The study deals with crop yields of bajra, kharif pulses and sesamum in relation to rainfall, mean relative humidity and mean temperature in Jodhpur district of western Rajasthan. Highest instability was observed in order to sesamum, bajra and kharif pulses, while mean relative humidity and mean temperature were more stable. Multivariate analysis showed that yield of crops under study were positive and significantly influenced by rainfall and negatively by mean temperature an Mon- barring bajra crop. The mean relative humidity had weak influence on crop productivity and significant at lower level. The order of variables were rainfall, mean relative humidity and mean temperature, for bajra and kharif pulses while for sesamum order was rainfall, mean temperature and mean relative humidity. Therefore, to increase the yield of crops, technological changes like use of fertilizer and HYV seeds more tolerant to temperature should be popularized.
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KAMARA, Serrie, Tilack KURUPPUARACHCHI, Edmond Ranga RANATUNGE, Yousay HAYASHI, Masayuki YOKOZAWA, Motoki NISHIMORI, and Takehiko MIKAMI. "Multivariate Statistical Analysis of the Seasonal Rainfall Regimes of the Guinea-Fouta Djallon Mountains of West Africa." Journal of Agricultural Meteorology 58, no. 4 (2002): 171–83. http://dx.doi.org/10.2480/agrmet.58.171.
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Sicard, Emeline, Robert Sabatier, Hélène Niel, and Eric Cadier. "A new approach in space-time analysis of multivariate hydrological data: Application to Brazil's Nordeste region rainfall." Water Resources Research 38, no. 12 (December 2002): 55–1. http://dx.doi.org/10.1029/2002wr001413.
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Halid, D. A., I. Atan, J. Jaafar, Y. Ashaari, S. N. Mohamed, M. B. Samsudin, and A. Baki. "The Learning of Multivariate Adaptive Regression Splines (MARS) Model in Rainfall-Runoff Processes at Pahang River Catchment." Annals of Valahia University of Targoviste, Geographical Series 18, no. 2 (October 1, 2018): 161–67. http://dx.doi.org/10.2478/avutgs-2018-0018.
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Abstract Recently, a novel data mining technique, Multivariate Adaptive Regression Splines (MARS) has begun attracted attention from several hydrological researchers because their application is relatively new in modelling hydrological processes. The power of this approach has been proven in variety learning problems such as financial analysis, species distributions modelling, and doweled pavement performance modelling. Therefore, the objective of this paper is to investigate the performance of MARS model in capture the rainfall-runoff processes at river catchment of Malaysia. Pahang River has been selected as area of study. 30-years data set of daily rainfall and runoff at upstream tributaries of Pahang River were used to developed and validate the capability of MARS model in flood prediction. The effect of different length of record data to performance of MARS model was also examined by arranged the data into 5-years data set, 10 years data set, 20 years data set, and 30 years data set. All these data sets used 1-year data of 2003 for validation process while the others were applied for calibration. Simulation results showed that MARS model was able to learn the rainfall-runoff processes in Pahang River catchment and the model performance improved due to the longer period of data.
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Takyi Appiah, Sampson, Albert Buabeng, and N. K. Dumakor-Dupey. "Multivariate Analysis of the Effect of Climate Conditions on Gold Production in Ghana." Ghana Mining Journal 18, no. 1 (June 28, 2018): 72–77. http://dx.doi.org/10.4314/gm.v18i1.9.
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The change in climatic conditions and its catastrophic effect on mining activities has become a source of worry for mining industries and therefore needs due attention. This study examined the effect some climate factors have on gold production in Ghana. First, a direct Multiple Linear Regression was applied on the climate factors with the aim of determining the relative effect of each factor on gold production which exhibited a time series structure. The consequence is that, the estimates of the coefficients and their standard errors will be wrongly estimated if the time series structure of the errors is ignored. In order to eliminate these deficiencies and better understand the effect of these climate factors on gold production, regression with ARIMA errors technique was employed after its appropriateness has been tested. The model was then compared in terms of prediction accuracy which resulted a MAPE of 9.78%. It was concluded that, gold production in Ghana is positively related to Temperature whilst negatively to Rainfall and Precipitate. It was recommended that mine operators in Ghana could base on this analysis to optimise their production planning and scheduling. Keywords: Gold Production, Climate, Multicollinearity, VIF, Regression Models with ARIMA Errors
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Valent, Peter, and Roman Výleta. "Continuous Simulation of Catchment Runoff in Flood Frequency Analysis: A Case Study from Slovakia." Proceedings 7, no. 1 (November 15, 2018): 16. http://dx.doi.org/10.3390/ecws-3-05828.
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Research questions relating to a reliable estimate of flood discharge have always interested both hydrologists and civil engineers. Over the decades, numerous methods have been proposed and used more or less successfully, all of them with known limitations restricting their use in a wide range of conditions and problems. In the past, the characteristics of hydrological extremes were mostly estimated by the methods of statistical analyses. As this type of method is not suitable to estimate design discharges of high return periods, and by default does not account for uncertainty, a new family of methods is slowly taking the place of the traditional approaches. Many of these methods are based on a combination of stochastic rainfall models (weather generators) and rainfall-runoff models, which enables generation of an arbitrary number of synthetic floods, even in places with short or no record of river discharges available. In addition, as this type of method produces flood hydrographs, they can also be used in a multivariate flood frequency analysis to estimate joint probabilities of two or more flood characteristics. This study presents a methodology for flood frequency analysis that combines stochastic models of both rainfall amounts and air temperatures with a lumped rainfall-runoff model to transfer the outputs of the stochastic models into a series of corresponding river discharges. Both of the stochastic models are single-site weather generators that produce continuous time series of mean areal daily rainfall amounts and air temperatures. In this study, the method was used to generate a time series of 10,000 years of mean daily discharges, which was used to build a flood frequency curve and to estimate extreme flood discharges of given return periods. The method was applied to a mountainous catchment of the River Váh in Slovakia.
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Tsai, Hsiao-Chung, and Tim Hau Lee. "Maximum Covariance Analysis of Typhoon Surface Wind and Rainfall Relationships in Taiwan." Journal of Applied Meteorology and Climatology 48, no. 5 (May 1, 2009): 997–1016. http://dx.doi.org/10.1175/2008jamc1963.1.
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Abstract The multivariate relationships between hourly surface wind and rainfall observations during typhoons affecting Taiwan have been investigated with maximum covariance analysis (MCA). Historical surface observations from 1987 to 2004 are used when typhoon centers were located inside the domain of 19°–28°N, 117°–127°E. The three leading MCA modes explain 70%, 20.6%, and 7.6% of the squared covariance fraction, and the correlation coefficients are 0.59, 0.48, and 0.49, respectively. The wind directions of the three leading positive modes are 1) northwesterly flow perpendicular to the Snow Mountain Range (SMR), 2) southwesterly flow toward the river valleys of the southwestern Central Mountain Range (CMR) and the southern SMR, and 3) easterly flow toward the northeastern SMR and the northern CMR. The rainfall patterns of the three principal modes reveal the contrast between the windward and the leeward sides of the mountain ranges. Based on the MCA singular vectors, historical typhoon surface wind patterns are categorized into major types. The results show that the three major wind types consist of 53% of the data, with 25%, 9%, and 19%, respectively, for these wind types. Furthermore, the analyses of the corresponding surface air temperatures, relative humidities, and air pressures also reveal contrasting patterns between the windward and leeward sides.
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Wałęga, Andrzej, and Leszek Książek. "The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment." Annals of Warsaw University of Life Sciences, Land Reclamation 47, no. 4 (December 1, 2015): 305–20. http://dx.doi.org/10.1515/sggw-2015-0033.
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Abstract The effect of a hydrological model structure and rainfall data on the accuracy of flood description in an upland catchment. The aim of this paper was to determine the influence of a hydrological model structure and rainfall- -related data on flood parameters obtained from a simulation. The study included an upland river Stobnica, right tributary of the Wisłok. The following assumptions were investigated: (i) the greater number of rainfall stations, the more accurate a flood description, i.e. the resulting hydrograph much better describes the actual flood, (ii) a distributed parameter model provides a more precise description of a catchment response to rainfall than a lumped parameter model. All calculations were performed using HEC-HMS 3.4 software. The analyses showed that increasing the number of rainfall stations slightly improved the model performance (by on average 4.1%). Furthermore, it was showed that in the catchment characterized by low topographical variability and stable land use, more reliable flood simulation results were obtained in the lumped parameter model than in the distributed parameter model. Considering the calibration process slightly improved the model performance, irrespective of its structure and the number of rainfall stations. Multivariate analysis of variance (MANOVA) revealed that the resulting differences in the model efficiency for individual variants were not significant. Considering limited empirical evidence on rainfall-runoff episodes, uncertainty of these results is probably high and thus they should be treated as a starting point for further studies.
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Sousa, Ricardo Silva de, Edson Alves Bastos, Milton José Cardoso, and Diléia Rocha Pereira. "Identification of drought-tolerant corn genotypes by multivariate analysis1." Pesquisa Agropecuária Tropical 48, no. 3 (December 2018): 204–11. http://dx.doi.org/10.1590/1983-40632018v4852122.
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ABSTRACT The identification of genotypes that are tolerant to water deficit is crucial for the maintenance of the agricultural production. This study aimed to evaluate the genotypic variation for drought tolerance among corn genotypes by means of multivariate analysis, as well as to identify hybrids with high grain yield under conditions of water deficit and full irrigation. For this purpose, an experiment was conducted in a randomized block design, with 36 corn hybrids, being 34 experimental elite and two commercial (controls) hybrids, under water deficit and full irrigation, during the reproductive stage, with four replications. The irrigation levels, added to the rainfall, totaled 691.6 mm under full irrigation and 490.8 mm under water deficit. The evaluation encompassed the leaf area index, leaf chlorophyll content, interval between male and female flowering, number of rows per ear, number of grains per ear, ear length, 100-grain weight, ear yield, ear index, total number of grains, number of ears per m2, grain yield at 13 % of moisture and water-use efficiency. The evaluated hybrids showed useful genetic diversity for drought tolerance. Four experimental elite hybrids (3G7395, 3G7415, 1G7034 and 3G7335) stood out under water deficit, showing a high grain yield performance, if compared to the average of the control hybrids.
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Matsushita, Kim, Ng, Moriyama, Igarashi, Yamamoto, Otieno, Minakawa, and Hashizume. "Differences of Rainfall–Malaria Associations in Lowland and Highland in Western Kenya." International Journal of Environmental Research and Public Health 16, no. 19 (September 30, 2019): 3693. http://dx.doi.org/10.3390/ijerph16193693.
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Many studies have reported a relationship between climate factors and malaria. However, results were inconsistent across the areas. We examined associations between climate factors and malaria in two geographically different areas: lowland (lakeside area) and highland in Western Kenya. Associations between climate factors (rainfall, land surface temperature (LST), and lake water level (LWL)) and monthly malaria cases from 2000 to 2013 in six hospitals (two in lowland and four in highland) were analyzed using time-series regression analysis with a distributed lag nonlinear model (DLNM) and multivariate meta-analysis. We found positive rainfall–malaria overall associations in lowland with a peak at 120 mm of monthly rainfall with a relative risk (RR) of 7.32 (95% CI: 2.74, 19.56) (reference 0 mm), whereas similar associations were not found in highland. Positive associations were observed at lags of 2 to 4 months at rainfall around 100–200 mm in both lowland and highland. The RRs at 150 mm rainfall were 1.42 (95% CI: 1.18, 1.71) in lowland and 1.20 (95% CI: 1.07, 1.33) in highland (at a lag of 3 months). LST and LWL did not show significant association with malaria. The results suggest that geographical characteristics can influence climate–malaria relationships.
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López-Sáez, José Antonio, Francisca Alba-Sánchez, Daniel Sánchez-Mata, Daniel Abel-Schaad, Rosario G. Gavilán, and Sebastián Pérez-Díaz. "A palynological approach to the study of Quercus pyrenaica forest communities in the Spanish Central System." Phytocoenologia 45, no. 1 (July 1, 2015): 107–24. http://dx.doi.org/10.1127/0340-269x/2014/0044-0572.
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A total of 75 surface samples collected from mosses in the Quercus pyrenaica forests of the Spanish Central System mountains were analysed for their pollen content. The samples were taken from six different Quercus pyrenaica phytosociological associations between 443 and 1657 m a.s.l. and fall within distinct rainfall and temperature regimes. The aims of this paper are to provide new data on the modern pollen rain from Central Spain, and to perform these data using multivariate statistics (hierarchical cluster analysis and principal component analysis) and pollen percentages. We could distinguish first between unaltered and disturbed forest landscapes and among different Quercus pyrenaica forest associations based on climatic gradients (rainfall pattern, summer moisture). This analysis allowed us to identify a set of pollen taxa markers which could assist in distinguishing these oak forest communities.