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Spatial and temporal variability of turbidity, dissolved oxygen, conductivity, temperature, and fluorescence in the lower Mekong River–Tonle Sap system identified using continuous monitoring
Continuous monitoring of turbidity, dissolved oxygen (DO), conductivity, temperature, and fluorescence was done at five locations on the Tonle Sap Lake and the Mekong–Bassac Rivers near Phnom Penh, Cambodia, between 2004 and 2010 using autonomous datasondes. Seasonal, daily, and spatial trends were...
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Published in: | International journal of river basin management 2011-06, Vol.9 (2), p.151-168 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Continuous monitoring of turbidity, dissolved oxygen (DO), conductivity, temperature, and fluorescence was done at five locations on the Tonle Sap Lake and the Mekong–Bassac Rivers near Phnom Penh, Cambodia, between 2004 and 2010 using autonomous datasondes. Seasonal, daily, and spatial trends were clearly identified in the data and were related to the annual monsoon rainy season–dry season cycle, system metabolism, system hydraulics, and in some cases, localized phenomena such as waste discharges. The datasondes were particularly useful to track the oxygenation of anoxic black water areas in the flooded forest fringe of the Tonle Sap that occurred during the rainy season freshwater pulse. A strongly developed vertical variation of turbidity, DO, and conductivity in the flooded forest fringe may be related to a combination of factors, including dissolved material release from bed sediment and a floating organic-rich particulate layer near the bottom of the lake. Grab samples for total suspended solids (TSS) were collected at the Preak Leap (PL) site (Mekong River) in 2009 and 2010. An excellent relationship was established between daily mean turbidity and TSS concentration for the PL site, with r² = 0.95. Autoregressive, integrated, moving average models adequately forecast water level and water quality data one month ahead. |
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ISSN: | 1814-2060 1571-5124 1814-2060 |
DOI: | 10.1080/15715124.2011.621430 |