Simulated Precipitation and Reservoir Inflow in the Chao Phraya River Basin by Multi-model Ensemble CMIP3 and CMIP5

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

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Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand

Water ◽

10.3390/w10111614 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1614 ◽

Cited By ~ 4

Author(s):

Somchit Amnatsan ◽

Sayaka Yoshikawa ◽

Shinjiro Kanae

Keyword(s):

River Basin ◽

Flood Control ◽

High Flow ◽

Low Flow ◽

Forecasting Method ◽

Forecasting Performance ◽

Inflow Forecasting ◽

Analogue Method ◽

Reservoir Inflow ◽

Chao Phraya River

Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture.

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The Chao Phraya River Basin: water quality and anthropogenic influences

Water Science & Technology Water Supply ◽

10.2166/ws.2018.167 ◽

2018 ◽

Vol 19 (5) ◽

pp. 1287-1294 ◽

Cited By ~ 2

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.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-6755-2015 ◽

2015 ◽

Vol 12 (7) ◽

pp. 6755-6797 ◽

Cited By ~ 4

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.

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Development of Hydrological Information System in the Chao Phraya River Basin

Japan Agricultural Research Quarterly JARQ ◽

10.6090/jarq.35.171 ◽

2001 ◽

Vol 35 (3) ◽

pp. 171-175 ◽

Cited By ~ 1

Author(s):

Yoshio HAYASE ◽

Kaichi KOSEKI ◽

Kumjon LAPCHAROEN ◽

Attaporn BUDDHAPALIT

Keyword(s):

Information System ◽

River Basin ◽

Chao Phraya River

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

JOURNAL OF JAPAN SOCIETY OF HYDROLOGY AND WATER RESOURCES ◽

10.3178/jjshwr.7.6_520 ◽

1994 ◽

Vol 7 (6) ◽

pp. 520-528 ◽

Cited By ~ 3

Author(s):

Wataru SHINTANI ◽

Kuniyoshi TAKEUCHI ◽

Vanchai SIVAARTHITKUL

Keyword(s):

Water Resources ◽

River Basin ◽

Counter Measures ◽

Chao Phraya River

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

Applied Environmental Research ◽

10.35762/aer.2020.42.3.8 ◽

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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