Search results for: multivariate disaggregation rainfall model

Authors: Ibrahim Suliman Hanaish

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Disaggregation daily rainfall using stochastic models formulated based on multivariate approach (MuDRain) is discussed in this paper. Seven rain gauge stations are considered in this study for different distances from the referred station starting from 4 km to 160 km in Peninsular Malaysia. The hourly rainfall data used are covered the period from 1973 to 2008 and July and November months are considered as an example of dry and wet periods. The cross-correlation among the rain gauges is considered for the available hourly rainfall information at the neighboring stations or not. This paper discussed the applicability of the MuDRain model for disaggregation daily rainfall to hourly rainfall for both sources of cross-correlation. The goodness of fit of the model was based on the reproduction of fitting statistics like the means, variances, coefficients of skewness, lag zero cross-correlation of coefficients and the lag one auto correlation of coefficients. It is found the correlation coefficients based on extracted correlations that was based on daily are slightly higher than correlations based on available hourly rainfall especially for neighboring stations not more than 28 km. The results showed also the MuDRain model did not reproduce statistics very well. In addition, a bad reproduction of the actual hyetographs comparing to the synthetic hourly rainfall data. Mean while, it is showed a good fit between the distribution function of the historical and synthetic hourly rainfall. These discrepancies are unavoidable because of the lowest cross correlation of hourly rainfall. The overall performance indicated that the MuDRain model would not be appropriate choice for disaggregation daily rainfall.

Keywords: rainfall disaggregation, multivariate disaggregation rainfall model, correlation, stochastic model

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Authors: Mohammad Bakhshi, Firas Al Janabi

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High resolution rain data are very important to fulfill the input of hydrological models. Among models of high-resolution rainfall data generation, the temporal disaggregation was chosen for this study. The paper attempts to generate three different rainfall resolutions (4-hourly, hourly and 10-minutes) from daily for around 20-year record period. The process was done by DiMoN tool which is based on random cascade model and method of fragment. Differences between observed and simulated rain dataset are evaluated with variety of statistical and empirical methods: Kolmogorov-Smirnov test (K-S), usual statistics, and Exceedance probability. The tool worked well at preserving the daily rainfall values in wet days, however, the generated data are cumulated in a shorter time period and made stronger storms. It is demonstrated that the difference between generated and observed cumulative distribution function curve of 4-hourly datasets is passed the K-S test criteria while in hourly and 10-minutes datasets the P-value should be employed to prove that their differences were reasonable. The results are encouraging considering the overestimation of generated high-resolution rainfall data.

Keywords: DiMoN Tool, disaggregation, exceedance probability, Kolmogorov-Smirnov test, rainfall

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Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju

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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events

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Authors: S. Ravindran, Y.Liu, I. Gratchev, D.Jeng

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Rainfall-induced shallow landslides are more common in the northern New South Wales (NSW), Australia. From 2009 to 2017, around 105 rainfall-induced landslides occurred along the road corridors and caused temporary road closures in the northern NSW. Rainfall causing shallow landslides has different distributions of rainfall varying from uniform, normal, decreasing to increasing rainfall intensity. The duration of rainfall varied from one day to 18 days according to historical data. The objective of this research is to analyse slope instability of some of the sites in the northern NSW by varying cumulative rainfall using SLOPE/W and SEEP/W and compare with field data of rainfall causing shallow landslides. The rainfall data and topographical data from public authorities and soil data obtained from laboratory tests will be used for this modelling. There is a likelihood of shallow landslides if the cumulative rainfall is between 100 mm to 400 mm in accordance with field data.

Keywords: landslides, modelling, rainfall, suction

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Jeffrey Tzu-Hao Tsai, Yi-Shan Wong

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This article proposes a cohort-age-period (CAP) model to characterize multi-population mortality processes using cohort, age, and period variables. Distinct from the factor-based Lee-Carter-type decomposition mortality model, this approach is empirically based and includes the age, period, and cohort variables into the equation system. The model not only provides a fruitful intuition for explaining multivariate mortality change rates but also has a better performance in forecasting future patterns. Using the US and the UK mortality data and performing ten-year out-of-sample tests, our approach shows smaller mean square errors in both countries compared to the models in the literature.

Keywords: longevity risk, stochastic mortality model, multivariate mortality rate, risk management

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Authors: Chisomo Patrick Kumbuyo, Katsuyuki Shimizu, Hiroshi Yasuda, Yoshinobu Kitamura

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This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. Zone 1 consisted of Karonga and Nkhatabay stations, located in northern Malawi; and Zone 2 consisted of Bolero, located in northern Malawi; Kasungu, Dedza, Salima, located in central Malawi; Mangochi, Makoka and Ngabu stations located in southern Malawi. Links between Malawi rainfall and SST based on statistical correlations were evaluated and significant results selected as predictors for the regression models. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Pacific Ocean. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r=0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa.

Keywords: Malawi rainfall, forecast model, predictors, SST

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Clarinda Vitorino Nhangumbe, Ercília Sousa

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Weather derivatives are financial products used to cover non catastrophic weather events with a weather index as the underlying asset. The rainfall weather derivative pricing model is modeled based in the assumption that the rainfall dynamics follows Ornstein-Uhlenbeck process, and the partial differential equation approach is used to derive the convection-diffusion two dimensional time dependent partial differential equation, where the spatial variables are the rainfall index and rainfall depth. To compute the approximation solutions of the partial differential equation, the appropriate boundary conditions are suggested, and an explicit numerical method is proposed in order to deal efficiently with the different choices of the coefficients involved in the equation. Being an explicit numerical method, it will be conditionally stable, then the stability region of the numerical method and the order of convergence are discussed. The model is tested for real precipitation data.

Keywords: finite differences method, ornstein-uhlenbeck process, partial differential equations approach, rainfall derivatives

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Authors: Ishapathik Das, Sumen Sen, N. Rao Chaganty, Pooja Sengupta

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Dependent multivariate count data occur in several research studies. These data can be modeled by a multivariate Poisson or Negative binomial distribution constructed using copulas. However, when some of the counts are inflated, that is, the number of observations in some cells are much larger than other cells, then the copula based multivariate Poisson (or Negative binomial) distribution may not fit well and it is not an appropriate statistical model for the data. There is a need to modify or adjust the multivariate distribution to account for the inflated frequencies. In this article, we consider the situation where the frequencies of two cells are higher compared to the other cells, and develop a doubly inflated multivariate Poisson distribution function using multivariate Gaussian copula. We also discuss procedures for regression on covariates for the doubly inflated multivariate count data. For illustrating the proposed methodologies, we present a real data containing bivariate count observations with inflations in two cells. Several models and linear predictors with log link functions are considered, and we discuss maximum likelihood estimation to estimate unknown parameters of the models.

Keywords: copula, Gaussian copula, multivariate distributions, inflated distributios

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo

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Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.

Keywords: convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Kamal Kumar Tanti, Nayan Moni Saikia, Markynti Swer

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This study aims to investigate and analyse the trends and variability of rainfall in Shillong and its nearby areas, located in Meghalaya hills of North-East India; which is geographically a neighbouring area to the wettest places of the Earth, i.e., Cherrapunji and Mawsynram. The analysis of variability and trends to annual, seasonal, monthly and daily rainfall was carried out, using the data collected from the IMD station at Shillong; thereby attempting to highlight whether rainfall in Shillong area has been increasing or decreasing over the years. Rainfall variability coefficient is utilized to compare the current rainfall trend of the area with its past rainfall trends. The present study also aims to analyse the frequency of occurrence of extreme rainfall events over the region. These studies will help us to establish a correlation between the current rainfall trend and climate change scenario of the study area.

Keywords: trends and variability of rainfall, annual, seasonal, monthly and daily rainfall, rainfall variability coefficient, extreme rainfall events, climate change, Shillong, Cherrapunji, Mawsynram

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Authors: Ahmed Najah Ahmed Al-Mahfoodh, Ali Najah Ahmed Al-Mahfoodh, Ahmed Al-Shafie

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In this study, genetic programming (GP) technique has been investigated in prediction of set of rainfall-runoff data. To assess the effect of input parameters on the model, the sensitivity analysis was adopted. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Correlation Coefficient (CC), Mean Square Error (MSE) and Correlation of Efficiency (CE). The principle aim of this study is to develop a computationally efficient and robust approach for predict of rainfall-runoff which could reduce the cost and labour for measuring these parameters. This research concentrates on the Johor River in Johor State, Malaysia.

Keywords: genetic programming, prediction, rainfall-runoff, Malaysia

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

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Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Ilaria Manca, Ilaria Manca, Francesca Pettinau, Ignazia Mocci, Elisabetta M. Usai, Barbara Pittau

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Lamotrigine is a phenyltriazine used in the treatment of epilepsy and bipolar disorder type I. The purpose of this study was to test and compare various solid forms of immediate release (IR) lamotrigine products, at different strenghts, in order to study their disaggregation and dissolution behavior. IR products are designed to release their active substance promptly after administration. Concentration of hydrochloric acid in gastric juice is about 0.1-0.001 M, so FDA (Food and Drug Administration) recommends, for lamotrigine regular tablets, dissolution tests in HCl 0.1 M.Toinvestigate the pH dependency of drug release in the entire gastrointestinal tract, we worked at two additional media with different pH values (4.5 and 6.8), that reflect conditions in it. To afford acceptable dissolution rates, tablets must disintegrate. Disaggregation of constituent particles increases the surface area and substantially increases the dissolution rate. For this reason availability of an active substance from tablets depends on its ability to disintegrate fast in dissolution media. pH of gastrointestinal fluid affects drug absorption by conditioning its solubility and dissolution, but also tablet disintegration may be influenced by it. To obtain information about the quantitative relationship between different mixture components, Nuclear Magnetic Resonance (NMR) spectroscopy was used. We also investigate tablet hardness. The investigation carried out confirms pH 1.2 as the ideal environment for the immediate availability of the active substance.

Keywords: dissolution, disaggregation, Lamotrigine, bioequivalence

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Authors: Kazım Kaba, Erdem Erdi, M. Akif Erdoğan, H. Mustafa Kandırmaz

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Rainfall is crucial data source for very different discipline such as agriculture, hydrology and climate. Therefore rain rate should be known well both spatial and temporal for any area. Rainfall is measured by using rain-gauge at meteorological ground stations traditionally for many years. At the present time, rainfall products are acquired from radar and satellite images with a temporal and spatial continuity. In this study, we investigated the accuracy of these rainfall data according to rain-gauge data. For this purpose, we used Adana-Hatay radar hourly total precipitation product (RN1) and Meteosat convective rainfall rate (CRR) product over Seyhan plain. We calculated daily rainfall values from RN1 and CRR hourly precipitation products. We used the data of rainy days of four stations located within range of the radar from October 2013 to November 2015. In the study, we examined two rainfall data over Seyhan plain and the correlation between the rain-gauge data and two raster rainfall data was observed lowly.

Keywords: meteosat, radar, rainfall, rain-gauge, Turkey

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: N. V. Nikhil, S. R. Lee, Do Won Park

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Korean peninsula receives about two third of the annual rainfall during summer season. The extreme rainfall pattern due to typhoon and heavy rainfall results in severe mountain disasters among which 55% of them are debris flows, a major natural hazard especially when occurring around major settlement areas. The basic mechanism underlined for this kind of failure is the unsaturated shallow slope failure by reduction of matric suction due to infiltration of water and liquefaction of the failed mass due to generation of positive pore water pressure leading to abrupt loss of strength and commencement of flow. However only an empirical model cannot simulate this complex mechanism. Hence, we have employed an empirical-physical based approach for hazard analysis of debris flow using TRIGRS, a debris flow initiation criteria and DAN3D in mountain Woonmyun, South Korea. Debris flow initiation criteria is required to discern the potential landslides which can transform into debris flow. DAN-3D, being a new model, does not have the calibrated values of rheology parameters for Korean conditions. Thus, in our analysis we have used the recent 2011 debris flow event in mountain Woonmyun san for calibration of both TRIGRS model and DAN-3D, thereafter identifying and predicting the debris flow initiation points, path, run out velocity, and area of spreading for future extreme rainfall based scenarios.

Keywords: debris flow, DAN-3D, extreme rainfall, hazard analysis

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Qiao Fan, Xiaobo Guo, Junxian Zhu, Xiaohu Ding, Ching-Yu Cheng, Tien-Yin Wong, Mingguang He, Heping Zhang, Xueqin Wang

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A systematic, simultaneous analysis of multiple phenotypes in genome-wide association studies (GWASs) draws a great attention to integrate the signals from single phenotypes with increased power. However, lacking an interpretable and efficient multivariate GWAS analysis impede the application of such approach. In this study, we propose to decompose the multivariate model into a series of simple univariate models. This transformation illuminates what exactly the individual trait contributes to the significant signals from the multivariate analyses. By employing our approach in the analysis of three myopia-related endophenotypes from the Singapore Malay Eye Study (SIMES), we identify novel candidate loci which were successfully validated in an independent Guangzhou Twin Eye Study (GTES).

Keywords: GWAS multivariate, multiple traits, myopia, association

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Authors: Gurjeet Singh, Rabindra K. Panda

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Soil moisture is a key hydrologic state variable and is intrinsically linked to the Earth's water, climate and carbon cycles. On ecological point of view, the soil moisture is a fundamental natural resource providing the transpirable water for plants. Soil moisture varies both temporally and spatially due to spatiotemporal variation in rainfall, vegetation cover, soil properties and topography. Satellite derived soil moisture provides spatio-temporal extensive data. However, the spatial resolution of a typical satellite (L-band radiometry) is of the order of tens of kilometers, which is not good enough for developing efficient agricultural water management schemes at the field scale. In the present study, the soil moisture from radiometer data has been disaggregated using blending approach to achieve higher resolution soil moisture data. The radiometer estimates of soil moisture at a 40 km resolution have been disaggregated to 10 km, 5 km and 1 km resolutions. The disaggregated soil moisture was compared with the observed data, consisting of continuous sensor based soil moisture profile measurements, at three monitoring sites and extensive spatial near-surface soil moisture measurements, concurrent with satellite monitoring in the 500 km2 study watershed in the Eastern India. The estimated soil moisture status at different spatial scales can help in developing efficient agricultural water management schemes to increase the crop production and water use efficiency.

Keywords: disaggregation, eastern India, radiometers, soil moisture, water use efficiency

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Authors: Luigi Delle Rose, Oliver Fischer, Ahmed Hammad

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In this article, we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to an SM-like Higgs boson and a heavy scalar particle. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques, we show that the LHeC is sensitive to heavy scalars with masses between 200 and 800 GeV down to scalar mixing of order 0.01.

Keywords: beyond the standard model, large hadron electron collider, multivariate analysis, scalar singlet

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Authors: Chiang Wei, Hui-Chung Yeh, Yen-Chang Chen

Abstract:

The objective of this study is to estimate the rainfall using the new generation Himawari-8 meteorological satellite with multi-band, high-bit format, and high spatiotemporal resolution, ground rainfall data at the Chen-Yu-Lan watershed of Joushuei River Basin (443.6 square kilometers) in Central Taiwan. Accurate and fine-scale rainfall information is essential for rugged terrain with high local variation for early warning of flood, landslide, and debris flow disasters. 10-minute and 2 km pixel-based rainfall of Typhoon Megi of 2016 and meiyu on June 1-4 of 2017 were tested to demonstrate the new generation Himawari-8 meteorological satellite can capture rainfall variation in the rugged mountainous area both at fine-scale and watershed scale. The results provide the valuable rainfall information for early warning of future disasters.

Keywords: estimation, Himawari-8, rainfall, satellite imagery

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