Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

Wuttichaikitcharoen, Piyawat; Babel, Mukand S.

doi:10.3390/w6082412

Cited by 42 publications

(21 citation statements)

References 46 publications

(60 reference statements)

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“…It is the net result of soil erosion and processes of sediment accumulation, so it depends on variables that control water and sediment discharge to reservoirs [2]. Sediment yield is influenced by many factors, which include topography, soil, climate, land use, and drainage characteristics [3][4][5][6]. The problem of sedimentation is aggravated by human activities and climate change.…”

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confidence: 99%

The Effect of Climate Change on Loss of Lake Volume: Case of Sedimentation in Central Rift Valley Basin, Ethiopia

et al. 2018

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Evaluating the impact of climate change on sediment yield has become one of the major topics in climate research. The purpose of this study was to investigate sediment yield contribution to lake volume change under changing climatic conditions in the Central Rift Valley Basin. The ensemble mean of five regional climate models (RCMs) in the coordinated regional climate downscaling experiment (CORDEX)-Africa was considered for the purpose of this study. The climate variables (precipitation, minimum and maximum temperatures) in RCMs were bias corrected against observed data using linear scaling (LS), power transformation (PT), variance of scaling (VS), and quantile mapping (QM). Two emission scenarios, the Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered for the future scenario period . Better results were obtained when the ensemble values of the bias correction methods were used. Hence, the projected values of climate variables after bias correction were used in the Soil and Water Assessment Tool (SWAT) hydrological model to estimate the sediment yield contribution to lake volume change due to climate change. The results show that the average projected precipitation will decrease by 7.97% and 2.55% under RCP4.5 and RCP8.5, respectively. On average, the maximum temperature will increase by 1.73 • C and 2.36 • C under RCP4.5 and RCP8.5, respectively, while the minimum temperature will increase by 2.16 • C and 3.07 • C under RCP4.5 and RCP8.5, respectively. The average annual sediment yield contributions to Lake Ziway were 431.05 ton/km 2 and 322.82 ton/km 2 for the Meki and Ketar rivers, respectively, in the historical period . The study also reveals that the annual sediment yield that was estimated for the Meki River was 323 ton/km 2 and 382 ton/km 2 under RCP4.5 and under RCP8.5, respectively. The sediment estimations for the Ketar River were 157 ton/km 2 and 211 ton/km 2 under RCP4.5 under RCP8.5, respectively. This will decrease the rate of volume change in Lake Ziway by 38% under RCP4.5 and by 23% under RCP8.5. The results show that the life expectancy of the lake is likely to increase under climate change scenarios. This will help water resources managers make informed decisions regarding the planning, management, and mitigation of the river basins.Hydrology 2018, 5, 67 2 of 18 reservoirs, ponds, and rivers. The sedimentation of the water bodies reduces their capacity and the useful lifespan of the reservoir. Sediment yield can be described as the amount of sediment that would enter into a reservoir located at the outlet of the basin [1]. It is the net result of soil erosion and processes of sediment accumulation, so it depends on variables that control water and sediment discharge to reservoirs [2]. Sediment yield is influenced by many factors, which include topography, soil, climate, land use, and drainage characteristics [3][4][5][6]. The problem of sedimentation is aggravated by human activities and climate change. A study conducted by Belete [7] on Lake Hawasa showed that sediment...

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The Effect of Climate Change on Loss of Lake Volume: Case of Sedimentation in Central Rift Valley Basin, Ethiopia

et al. 2018

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“…The construct validity of the measurement items was checked by applying principal component analysis (PCA) with an acceptable minimum level of 0.5 for the component loadings of the items. PCA uses a statistical technique to reduce a large number of variables to a small number for ease of analysis (Wuttichaikitcharoen and Babel 2014). The result of the PCA for all the variables is displayed in Appendix A.…”

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The relationship between knowledge management and nursing care performance

Ajanaku

Mutula

2018

SAJLIS

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The role of knowledge management in addressing the quality of care rendered by registered nurses has received little attention in Nigerian teaching hospitals. This study investigated the relationship between knowledge management and performance of nursing care in selected teaching hospitals. A quantitative study comprising of a survey design was employed. Data was collected from 320 registered nurses using the proportionate stratified sampling method from two purposively selected teaching hospitals in South West, Nigeria. Questionnaires were distributed to registered nurses across the clinical units in the selected teaching hospitals. Structural equation modelling using SPSS version 22 was used to test the hypothesised relationships. The findings revealed that information technology, organisational structure and organisational culture in knowledge management infrastructure are found to positively and significantly influence knowledge management processes (knowledge acquisition, conversion, application and protection) in the selected teaching hospitals. The study further revealed that the mutual relationship between the dimensions of knowledge management infrastructure and knowledge management process strongly and significantly influence nursing care performance in the teaching hospitals. Knowledge management infrastructure and process can contribute to the overall performance of nursing care and simultaneously improve the performance of teaching hospitals.

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“…The climate of the basin is characterized by average annual rainfall of 1,097 mm (Sharma et al, 2007). The Ping River length is approximately 740 km (Wuttichaikitcharoen & Babel, 2014) with the river slope varying from 1:40 to 1:2,300 (Hydro and Agro Informatics Institute [Haii], 2014).…”

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The effects of Thailand׳s Great Flood of 2011 on river sediment discharge in the upper Chao Phraya River basin, Thailand

Bidorn

Chanyotha

Kish

et al. 2015

International Journal of Sediment Research

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Severe flooding that occurred during the 2011 monsoon season in Thailand was the heaviest flooding in the past 50 years. The rainfall over the northern part of Thailand, especially during July-August 2011, was 150 percent higher than average. During the flooding period, river flows of the four major Chao Phraya River tributaries (Ping, Wang, Yom, and Nan rivers) increased in the range of 1.4 to 5 times the average discharge. This study examined the river sediment discharge of the four major rivers in the upper Chao Phraya River basin in Thailand. The four rivers are considered the main sources of sediment supply to the Chao Phraya Estuary. River surveys of the Ping, Wang, Yom, and Nan rivers were carried out in October 2011 (during the great flood) and October 2012 (one year after the flood). Survey data included river cross sections, flow velocities, suspended sediment concentrations, and bed load transport in each river. Analyses of these data indicated that total sediment transport rates for the four main rivers during the flooding of 2011 were 2.3-5.6 times higher than the average sediment discharge over 60 years. The Flood of 2011 significantly affected the sediment characteristics including the proportions of suspended and bed sediment loads in each river though in different ways. The rates of sediment transport per unit discharge for the Ping and Wang rivers dramatically increased during the 2011 Flood, but the flooding had minimal effects on the sediment characteristics in the Yom and Nan rivers. The amount of total sediment discharge in each river caused by the 2011 flooding varied between 0.3 and 1.6 Mt. Additionally, the bed load transport in these rivers varied between 0% and 26% of the suspended sediment discharge.

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Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

Cited by 42 publications

References 46 publications

The Effect of Climate Change on Loss of Lake Volume: Case of Sedimentation in Central Rift Valley Basin, Ethiopia

The Effect of Climate Change on Loss of Lake Volume: Case of Sedimentation in Central Rift Valley Basin, Ethiopia

The relationship between knowledge management and nursing care performance

The effects of Thailand׳s Great Flood of 2011 on river sediment discharge in the upper Chao Phraya River basin, Thailand

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