BIAS CORRECTION OF RUNOFF DATA IN AGCM3.2S FOR UPPER CHAO PHRAYA RIVER BASIN, THAILAND

2020 ◽  
Vol 76 (5) ◽  
pp. I_55-I_63
Author(s):  
Teerawat RAM-INDRA ◽  
Yasuto TACHIKAWA ◽  
Kazuaki YOROZU ◽  
Yutaka ICHIKAWA
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Chao Phraya River

AGCM3.2S RUNOFF DATA BIAS CORRECTION OVER UPPER CHAO PHRAYA RIVER BASIN BASED ON LAND COVER GROUPING

2020 ◽  
Vol 76 (2) ◽  
pp. I_91-I_96
Author(s):  
Teerawat RAM-INDRA ◽  
Yasuto TACHIKAWA ◽  
Kazuaki YOROZU ◽  
Yutaka ICHIKAWA
Keyword(s):  
Land Cover ◽  
River Basin ◽  
Bias Correction ◽  
Chao Phraya River

scholarly journals Bias correction techniques for meteorological data of A2 scenario climate model output in Chao Phraya River Basin of Thailand

2014 ◽  
Vol 8 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Somchai Baimoung ◽  
Taikan Oki ◽  
Boonlert Archevarahuprok ◽  
Aphantree Yuttaphan ◽  
Manoon Pangpom
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Climate Model ◽  
Meteorological Data ◽  
Model Output ◽  
Climate Model Output ◽  
Chao Phraya River

ANN-Based Bias Correction Algorithm for Precipitation in the Yarra River Basin, Australia

2017 ◽  
pp. 362-370
Author(s):  
P. Saravanan ◽  
C. Sivapragasam ◽  
M. Nitin ◽  
S. Balamurali ◽  
R. K. Ragul ◽  
...  
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Algorithm ◽  
Yarra River

The Chao Phraya River Basin: water quality and anthropogenic influences

2018 ◽  
Vol 19 (5) ◽  
pp. 1287-1294 ◽  
Author(s):  
Nuanchan Singkran ◽  
Pitchaya Anantawong ◽  
Naree Intharawichian ◽  
Karika Kunta
Keyword(s):  
Water Quality ◽  
Land Use ◽  
River Basin ◽  
River Mouth ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Paddy Fields ◽  
Coliform Bacteria ◽  
Wet Season ◽  
Chao Phraya River

Abstract Land use influences and trends in water quality parameters were determined for the Chao Phraya River, Thailand. Dissolved oxygen (DO), biochemical oxygen demand (BOD), and nitrate-nitrogen (NO3-N) showed significant trends (R2 ≥ 0.5) across the year, while total phosphorus (TP) and faecal coliform bacteria (FCB) showed significant trends only in the wet season. DO increased, but BOD, NO3-N, and TP decreased, from the lower section (river kilometres (rkm) 7–58 from the river mouth) through the middle section (rkm 58–143) to the upper section (rkm 143–379) of the river. Lead and mercury showed weak/no trends (R2 < 0.5). Based on the river section, major land use groups were a combination of urban and built-up areas (43%) and aquaculture (21%) in the lower river basin, paddy fields (56%) and urban and built-up areas (21%) in the middle river basin, and paddy fields (44%) and other agricultural areas (34%) in the upper river basin. Most water quality and land use attributes had significantly positive or negative correlations (at P ≤ 0.05) among each other. The river was in crisis because of high FCB concentrations. Serious measures are suggested to manage FCB and relevant human activities in the river basin.

scholarly journals Distributed model of hydrological and sediment transport processes in large river basins in Southeast Asia

2015 ◽  
Vol 12 (7) ◽  
pp. 6755-6797 ◽  
Author(s):  
S. Zuliziana ◽  
K. Tanuma ◽  
C. Yoshimura ◽  
O. C. Saavedra
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Distributed Model ◽  
Mekong River Basin ◽  
Chao Phraya River ◽  
Large River Basins

Abstract. Soil erosion and sediment transport have been modeled at several spatial and temporal scales, yet few models have been reported for large river basins (e.g., drainage areas > 100 000 km2). In this study, we propose a process-based distributed model for assessment of sediment transport at a large basin scale. A distributed hydrological model was coupled with a process-based distributed sediment transport model describing soil erosion and sedimentary processes at hillslope units and channels. The model was tested on two large river basins: the Chao Phraya River Basin (drainage area: 160 000 km2) and the Mekong River Basin (795 000 km2). The simulation over 10 years showed good agreement with the observed suspended sediment load in both basins. The average Nash–Sutcliffe efficiency (NSE) and average correlation coefficient (r) between the simulated and observed suspended sediment loads were 0.62 and 0.61, respectively, in the Chao Phraya River Basin except the lowland section. In the Mekong River Basin, the overall average NSE and r were 0.60 and 0.78, respectively. Sensitivity analysis indicated that suspended sediment load is sensitive to detachability by raindrop (k) in the Chao Phraya River Basin and to soil detachability over land (Kf) in the Mekong River Basin. Overall, the results suggest that the present model can be used to understand and simulate erosion and sediment transport in large river basins.

scholarly journals Development of Hydrological Information System in the Chao Phraya River Basin

2001 ◽  
Vol 35 (3) ◽  
pp. 171-175 ◽  
Author(s):  
Yoshio HAYASE ◽  
Kaichi KOSEKI ◽  
Kumjon LAPCHAROEN ◽  
Attaporn BUDDHAPALIT
Keyword(s):  
Information System ◽  
River Basin ◽  
Chao Phraya River

scholarly journals Counter-Measures Against Water Resources Crisis in the Chao Phraya River Basin

1994 ◽  
Vol 7 (6) ◽  
pp. 520-528 ◽  
Author(s):  
Wataru SHINTANI ◽  
Kuniyoshi TAKEUCHI ◽  
Vanchai SIVAARTHITKUL
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Counter Measures ◽  
Chao Phraya River

scholarly journals Radar Quality Index for a Mosaic of Radar Reflectivity over Chao Phraya River Basin, Thailand

2020 ◽  
pp. 92-104
Author(s):  
Nattapon Mahavik ◽  
Sarintip Tantanee
Keyword(s):  
River Basin ◽  
Quality Index ◽  
Beam Propagation ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Temporal Information ◽  
Radar Beam ◽  
Spatio Temporal ◽  
Chao Phraya River ◽  
Application Fields

The weather radar is one of the tools that can provide spatio-temporal information for nowcast which is useful for hydro-meteorological disasters warning and mitigation system. The ground-based weather radar can provide spatial and temporal information to monitor severe storm over the risky area. However, the usage of multiple radars can provide more effective information over large study area where single radar beam may be blocked by surrounding terrain Even though, the investigation of the sever storm physical characteristics needs the information from multiple radars, the mosaicked radar product has not been available for Thai researcher yet. In this study, algorithm of mosaicked radar reflectivity has been developed by using data from ground-based radar of Thai Meteorological Department over the Chao Phraya river basin in the middle of Thailand. The Python script associated with OpenCV and Wradlib libraries were used in our investigations of the mosaicking processes. The radar quality index (RQI) field has been developed by implementing an equation of a quality radar index to identify the reliability of each mosaicked radar reflectivity pixels. First, the percentage of beam blockage is computed to understand the radar beam propagation obstructed by surrounding topography in order to clarify the limitations of the observed beam on producing radar reflectivity maps. Second, the elevation of beam propagation associated with distance field has been computed. Then, these three parameters and the obtained percentage of beam blockage are utilized as the parameters in the equation of RQI. Finally, the detected radar flare, non-precipitating radar area, has been included to the RQI field. Then, the RQI field has been applied to the extracted radar reflectivity to evaluate the quality of mosaicked radar reflectivity to inform end user in any application fields over the Chao Phraya river basin.

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