Dry season water allocation in the Chao Phraya River basin, Thailand

Takeda, Masayuki; Laphimsing, Athit; Putthividhya, Aksara

doi:10.1080/07900627.2015.1055856

Cited by 18 publications

(14 citation statements)

References 19 publications

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“…The rainy season is from May to October, the cool season is from November to mid-February, and the hot season is from mid-February to the beginning of May [23]. The mean annual precipitation in the Chao Phraya River basin is 1487.4 mm, 90% of which falls during the rainy season [24]. The main river from the confluence of four upstream waterways to the river mouth is 396 km long, which can be divided into three sections in terms of river classification based on water quality standards: lower (river km (RKM) 7 to 62), middle (RKM 62 to 142), and upper (RKM 142 to 379) [25].…”

Section: Study Areamentioning

confidence: 99%

Duality of Seasonal Effect and River Bend in Relation to Water Quality in the Chao Phraya River

Huang

Han

Liu

et al. 2019

Water

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The present study conducted a field survey of water quality along the Chao Phraya River during the past three years. The main objective was to better understand the spatial–temporal variations in water quality in relation to season and channel morphology. It assessed the water quality in terms of chemical parameters, bacteria, and phytoplankton. The results revealed a duality of seasonal effect for nutrients. The rainy season degraded the water quality by increasing the nutrient concentration in the waterway in the beginning, but cleaned it up by dilution in the end. However, this duality did not apply to Escherichia coli (E. coli), for which the highest level occurred during the second half of the rainy season and a sag curve variation pattern was displayed along the mainstream. Another duality found by this study is that there was no statistically significant difference in water quality in terms of chemical parameters between a river bend and the straight channel shortcutting the bend, but significant differences in the level of E. coli and the phytoplankton community structure were observed between the two. Of particular note, the present study revealed a coexistence of a saproxenous species (algae found in clean water) with a harmful species in the bend river reach.

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Section: Study Areamentioning

confidence: 99%

Duality of Seasonal Effect and River Bend in Relation to Water Quality in the Chao Phraya River

Huang

Han

Liu

et al. 2019

Water

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“…Along with the Nan River, the Ping is one of the main tributaries of the Chao Phraya, whose basin covers 30% of the country's surface (Figure 1a). The water flowing in the Chao Phraya Basin serves multiple users—i.e., agricultural, industrial, and domestic supply, hydropower generation, navigation, and prevention of seawater intrusion in the Gulf of Thailand—supporting a population of approximately 25 million people, including 8 million in Bangkok (Divakar et al, 2011; Takeda et al, 2016). A key component of the water system is the Bhumibol Reservoir, located on the Ping River.…”

Section: Study Site and Datamentioning

confidence: 99%

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

Nguyen

Galelli

2018

Water Resources Research

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Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation‐Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45–497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime‐like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree‐rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

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“…Along with the Nan River, the Ping is one of the main tributaries of the Chao Phraya, whose basin covers 30% of the country's surface ( Figure 1a). The water flowing in the Chao Phraya Basin serves multiple users-i.e., agricultural, industrial, and domestic supply, hydropower generation, navigation, and prevention of seawater intrusion in the Gulf of Thailand-supporting a population of approximately 25 million people, including 8 million in Bangkok (Divakar et al, 2011;Takeda et al, 2016). A key component of the water system is the Bhumibol Reservoir, located on the Ping River.…”

Section: The Ping River Basinmentioning

confidence: 99%

A linear dynamical systems approach to streamflow reconstruction reveals history of regime shifts in northern Thailand

Nguyen¹,

Galelli²

2017

Preprint

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Key points• The state variable reveals regime-like behavior in the catchment history• The linear dynamic model has higher accuracy than conventional linear regression • The model can generate stochastic replicates of both streamflow and catchment state time series ABSTRACT Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleo-climatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record-the Monsoon Asia Drought Atlas-to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores increase by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history-e.g., regime-like behavior-thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

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Dry season water allocation in the Chao Phraya River basin, Thailand

Cited by 18 publications

References 19 publications

Duality of Seasonal Effect and River Bend in Relation to Water Quality in the Chao Phraya River

Duality of Seasonal Effect and River Bend in Relation to Water Quality in the Chao Phraya River

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

A linear dynamical systems approach to streamflow reconstruction reveals history of regime shifts in northern Thailand

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