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    Effectiveness of Horizontal Sub-drain for Slope Stability on Crack Soil Using Numerical Model
    (2023-11-01)
    Sari, Putu Tantri Kumala
    ;
    Mochtar, Indrasurya B.
    ;
    Chaiyaput, Salisa
    A landslide occurred in the hilly area of Tulakan District, Pacitan, East Java Province, Indonesia. This was due to a period of heavy rain, resulting in a cumulative intensity of over 1000 mm in one month and a maximum daily rainfall exceeding 300 mm. Previous reports have suggested the use of horizontal sub-drains to manage groundwater levels and rainwater seepage to reduce the impact on slope stability. Therefore, this study aimed to determine the effectiveness of horizontal sub-drain as an alternative for managing groundwater and rainwater that seeped into the soil to increase the slope factor of safety by using numerical model. It also considered various factors such as the effect of real-time rainfall over a 30-day period before the landslide, hydraulic conductivity, soil parameter due to cracking and weathering, and the existing groundwater level. The coupled programs SEEP/W and SLOPE/W were used for analyses. The result showed that the horizontal sub-drain only increased the safety factor by less than 2% in the presence of a vertical crack and up to 7.7% with vertical cracks and weak layers in high ground water levels. In addition, this study found that horizontal sub-drains could be more effective in increasing the safety factor up to 11.5% when the rainfall intensity was higher (between 1.41 × 10<sup>–0.5</sup> and 1.85 × 10<sup>–0.7</sup> m/s) and lasted for 14 days. The installation position of the drains, soil conditions, rainfall condition, and contour topography were some of the factors that influenced the effectiveness of the horizontal sub-drains in increasing slope stability.
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    Wet and dry periods in the state of Alagoas (Northeast Brazil) via Standardized Precipitation Index
    (2021-11-01)
    Oliveira-Júnior, José Francisco de
    ;
    de Gois, Givanildo
    ;
    Silva, Iago José de Lima
    ;
    de Oliveira Souza, Edson
    ;
    Jardim, Alexandre Maniçoba da Rosa Ferraz
    The state of Alagoas has its economic base directed to agriculture, and the knowledge of wet and dry periods is fundamental for the success of agricultural enterprises. The objective was to define the wet and dry periods in the state of Alagoas via multivariate analysis applied to the Standardized Precipitation Index (SPI). Monthly rainfall data for 20 weather stations from 1960 to 2016 were used, with fault filling via imputation. Moreover, Cluster analysis (CA) was applied to the time series (definition of homogeneous regions) and SPI-12 (annual) - (determination of dry and wet spell periods). The annual accumulated rainfall showed two distinct periods: 1960 to 1990 (P1) and 1990 to 2016 (P2), according to Pettitt test. Both periods alternate according to the phases of La Niña and Neutral (rainy years) and El Niño (dry years). In the monthly rainfall, only the Eastern part of Alagoas increased the rainy months during long time series. In tested connection methods, the average connection, also known as cophenetic correlation coefficient (CCC = 0.8760), represented homogeneous groups of rainfall, featuring two regions: one on the coast (G<inf>1</inf>) and another hinterland and arid (G<inf>2</inf>); and two non-homogeneous groups (NA) in the state. The annual SPI helped identify dry and wet periods, regardless of El Niño-Southern Oscillation (ENSO) categorization. The SPI-12 categories show high annual and decadal variability in the groups, except for the extremely dry and wet categories. There is greater variability in dry and wet periods near the coast than hinterland of the state.
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    Impact of rainfall rate and temperature during fruit development on chilling injury of Queen pineapples (Ananas comosus L.) during cold storage
    (2021-10-01)
    Youryon, Pannipa
    ;
    Supapvanich, Suriyan
    In this study, we investigated the impact of rainfall rate and temperature during fruit development on chilling injury (CI) of Queen pineapples (Ananas comosus L. Merr) after cold storage at 13 °C. The pineapples were induced to flower in October 2016 and May 2017. Rainfall and field temperature during fruit development were recorded. The fruit were harvested in March 2017 and October 2017 (135 d after anthesis), respectively. Visual appearance, CI score, lightness (L*) value, parameters related to membrane integrity, and the activities of enzymes related to browning of the tissue adjacent to the core were monitored in the fruit after 7 and 14 d of cold storage followed by room temperature storage (27 ± 1 °C) (RT) for 2 d. High CI incidence was associated with high rainfall rather than the field temperature (approx. 27–29 °C) during fruit development. The fruit harvested in March experienced higher rainfall during fruit development than the fruit harvested in October. The CI severity of the fruit harvested in March was greater than that of fruit harvested in October. The higher CI severity of the fruit harvested in March was accompanied with reduced membrane integrity as well as enzymatic browning compared to the fruit harvested in October. In conclusion, high rainfall during fruit development is a major factor for CI of Queen pineapple grown in tropical zones. The results of this investigation provide insight into the causes underlying the loss of fruit quality during storage and marketing.
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    Abiotic and biotic factors affecting the occurrence of thrips on lotus flowers
    (2018-01-01)
    Bumroongsook, S.
    Frankliniella schultzei Trybom and Scirtothrips dorsalis Hood are two important insect pests of lotus flowers (Nulumbo nucifera Gearn). Both are active throughout the year and are difficult to control during pre-and post-harvest management. This study is focused on factors that influence the relationship among the thrips, their host plants and abiotic factors. Therefore, the distribution of these thrips species within lotus flowers of different flower ages was assessed using randomly sampling from commercially growing areas in Bangkok, Thailand and investigated thrips abundance on these flowers every week from April to September, 2016. The results revealed that there was a higher thrips population on older lotus flowers. S. dorsalis was much more abundant in summer than in the rainy season. It was found mainly on the pedicel and the outer corolla of the flowers. In the summer and rainy season, F. schultzei occurred most often and were found on the distal parts of lotus petals and pollen areas. Hindu lotus and East Indian lotus could support more of F. schultzei and S. dorsalis than Album Plenum and Roseum Plenum. The correlation among abiotic factors (temperature, amount of rainfall and relative humidity) and population fluctuations of S. dorsalis and F. schultzei was assessed. The studies showed a positive correlation of temperature (r = 0.7918) and a negative correction of relative humidity (r =-0.7588) to population of S. dorsalis but no relation with amount of rainfall (r = 0.0260). Whereas, there were non-significant correlations among F. schultzei and temperature (r = 0.43650, relative humidity (r =-0.2113) and rainfall (r =-0.4164). Interactions between different temperatures, relative humidity and rainfall effects on the abundance of the thrips were seen here. Both fundamental and applied perspectives will provide better understanding the thrips population abundance, and help to develop management plans for thrips control.
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    Statistical downscaling for rainfall and temperature prediction in Thailand
    (2011-07-26)
    Aksornsingchai, Pawanrat
    ;
    Srinilta, Chutimet
    This paper studies three statistical downscaling methods to predict temperature and rainfall at 45 weather stations in Thailand. Methods under consideration are multiple linear regressions (MLR), support vector machine with polynomial kernel (SVM-POL), and support vector machine with Radial Basis Function kernel (SVM-RBF). Large-scale data are from Geophysical Fluid Dynamics Laboratory (GFDL). Five predictor variables are chosen: (1) temperature, (2) pressure, (3) precipitation, (4) evaporator, and (5) net short wave. Accuracy is assessed by 10-fold cross-validation in terms of root-mean-squared error (RMSE) and correlation coefficient (R). SVM-RBF is the most accurate model. Prediction accuracy of monthly average rainfall and temperature is satisfying in most part of the country. Lastly, downscaling models can project long term trends of monthly average rainfall and temperature.