Understanding Water and Sediment Flux at the Chaktomuk Junction Towards Partial Completion of an Msc Sustainability at the University of Southampton

Author: Chloe Nunn 03-05-2018 Understanding Water and Sediment Flux at the Chaktomuk Junction Towards partial completion of an MSc Sustainability at the University of Southampton Introduction During the Mekong’s wet season, peak flow reaches approximately 1.2x1012 litres of water per day (Darby et al 2016) which is equivalent to the average daily water use of 2 billion Americans, or 63 billion Cambodians (Shen 2010). Despite physically having enough water every day for approximately 4,000 times its population, Cambodians and millions of others living near the Mekong still face problems when it comes to accessing clean water, and sustaining their river dependent livelihoods (Cook et al. 2009). Not only do residents of the Mekong watershed have to adapt to severe flooding for prolonged periods of the year caused by tropical cyclones, they must also adapt to anthropogenic impacts on the river, such as damming, which impacts sediment flux (Kummu & Varis 2007), and fish migration. The former maintains deltaic growth (Darby et al. 2016), provides nutrients to support ecosystem productivity (Wild & Loucks 2014), and builds up deposits used by fish (Baran & Guerin 2012). Whilst the latter means fish cannot reach spawning grounds to maintain their population, and consequently stocks are reduced, negatively impacting the financial and subsistence stability of individuals relying on fishing to make a living and feed their families (Nittrouer et al. 2017). Climate change is an established effect of anthropogenic activities and will likely manifest itself as increased weather variability (Kirtman et al. 2013). This, along with plans for extensive damming, is set to bring about unknown change to flow and sediment dynamics heavily relied upon on the Mekong. With increasing subsidence at the delta mouth caused by climate related sea level rise (M. Allison et al. 2017; Minderhoud et al. 2017; Nittrouer et al. 2017), a reduced sediment flux will only compound this problem, putting more people and their livelihoods at risk. Author: Chloe Nunn 03-05-2018 Figure 1. Phnom Penh, Cambodia and the Chaktomuk Junction on the Mekong River. By quantifying sediment flux and flow dynamics of the Mekong across multiple years and seasons, researchers will be better prepared to give management recommendations to government bodies in relation to future climate uncertainty. This study provides data on water and sediment flux at the Chaktomuk Junction, Phnom Penh, Cambodia (Figure 1) for the dry season. Currently, there is a lack of data at high spatial and temporal resolutions to determine sediment dynamics (M. A. Allison et al. 2017). Adding to the existing timeseries, the study also compliments research conducted over previous years during the wet seasons, filling in missing data points. Author: Chloe Nunn 03-05-2018 The aim of this study is to develop an understanding of flow dynamics at the Chaktomuk Junction during the dry season and to do so, will answer the following questions: 1. What is the water flux through the tributaries and distributaries at the Junction? a. Do these add up correctly? 2. What is the sediment flux through the tributaries and distributaries at the Junction? 3. How do the values compare to previous records? 4. How do the values compare to wet season values? 5. Do the values agree with relevant literature, and what might they implicate for the future of the Mekong dynamics and individual livelihoods? The following sections will outline the methodology used collecting and processing the data, as well as limitations. Visualisations of the data will be analysed, then discussed in the context of the overall aim, and sub-questions of the study. Finally, implications and conclusions will be drawn. Methods The fieldwork was carried out on a river cruise boat making transects across six different sections of the river, shown in Figure 2. Each was repeated four times, with the exception of the Bassac River which was repeated six times as it was so narrow. Crossings needed to be repeated until the difference between the total flux for each repeat crossing was less than 5% of the average total flux. Four worked for most transects apart from the Bassac because half of the transect was used for turning the vessel, skewing the data. To measure water velocity and acoustic backscatter (needed for sediment flux calculations) a Teledyne Acoustic Dopplar Current Profiler (ADCP) was used at 600kHz. It was attached to the port bow on a structure which could be raised and lowered (Figure 3). The instrument was powered by a car battery and communicated with a laptop via a cable. WinRiver software on the laptop was used to configure and run the ADCP, as well as display and save the collected data. Sediment samples were collected using a Van Doorn sampler (Figure 5). The instrument was lowered to the desired depth, a manual trigger was fired, and the instrument was recovered to the boat deck where the sample was carefully transferred into sample bottles, ensuring as little sediment was lost as possible. One litre of each sample was measured and then transferred to the filtration system (Figure 4), consisting of several glass flasks and a pump, all connected in series to one another via rubber tubing. The pump filtered the sample through glass microfibers (GF/C, 47mm diameter) and collected the water in the flasks. The final filter paper was stored for further analysis in the lab. The remainder of the liquid sample, which did not go through the filtration system, was used with a ‘Laser in-situ Scatterometry and Transmissometry’ (LISST) sensor to measure sediment concentration and particle size Figure 2. Location of transects. distribution. Author: Chloe Nunn 03-05-2018 The filter papers were weighed in the lab and their pre-sediment weight was subtracted, giving the weight of the sediment collected. Using the weight in conjunction with the volume of the sample, the sediment concentration was calculated. The concentration was plotted against the corresponding acoustic backscatter value, obtained from the ADCP. The same had been done during a previous study to the Vietnamese Mekong using the same instrumentation and was combined with the data collected for this study. This dataset was imported to Matlab and using the Curve Fitting Toolbox, a power regression was carried out giving an equation of good fit for the data. WinRiver was used to process the data collected with the ADCP. ASCII files were created and exported to the Velocity Mapping Toolbox (a Matlab function). The transects obtained at each station were averaged together. Using Dr Chris Hackney’s Matlab script (Appendix II) in conjunction with a power regression equation, and the averaged transects, a table (Table 1) of sediment fluxes was produced. Figures showing velocity and acoustic backscatter were produced using the Velocity Mapping Toolbox. Many limitations in this study arise from the methodology. They can be split into technical, and human error. Technical: See Kostaschuk et al (2005) for a discussion of the limitations arising from the ADCP. In brief, the instrument cannot measure all portions of the water column across the river and therefore must extrapolate out to measure the missing portions. This creates error in the total flux calculations. Human: Due to the fact that the boat used could not motor up to the river bank, a researcher was required to estimate the distance between the shore and where the boat carried out its turn. This was input to the ADCP (see technical limitations). In addition, when conducting the water sampling to obtain sediment concentration a researcher was required to lower the Van Doorn sampler to the desired depth. However, due to current flow it was difficult to ensure the rope (measuring the depth) was vertical in the water column. Therefore, uncertainty around the exact depth at which the sample was obtained from exists. This study was conducted over two days in January. While both days may have been characteristic of dry season weather and river dynamics, the data is still time limited. For the purposes of this study the data collected will be treated as ‘dry season’ data, however, this is a limitation when it comes to comparing it to other years and seasons as it is only a snapshot of dry season data for 2018. Figure 3. ADCP mounted on bow. Figure 4. Filtration system. Figure 5. Van Doorn Sampler Author: Chloe Nunn 03-05-2018 Results and Discussion ADCP profiles of current velocity and direction (not included due to the limited scope of this study), and acoustic backscatter were obtained for every crossing made at each transect, however there would be too many to discuss within the scope of this report so only the Chaktomuk Junction transect showing acoustic back scatter data will be analysed. All of the averaged backscatter data can be found in Appendix III. Figure 6 shows the transects with corresponding averaged velocity data and total water flux shown underneath, given with a two standard deviations margin of error. Figure 3. Averaged current velocity data for each transect. Total flux 'Q' (m3/s) is given below with two standard deviations as a margin of error. Red signifies faster flow and blue signifies slower flow. All transects are facing downstream. The total water flux results from the Mekong True Left Bifurcation and Mekong True Right Bifurcation were used to assess the instrument, as the amounts of water flowing through each should add up to the total flux of the Mekong Upstream value. Doing these calculations gives: (3800 ± 255) + (1900 ± 93) = 5700 ± 352 Considering the margin of error of the calculation, the measurements are satisfactory. The same calculations can be solved for other combinations; Tonle Sap + Mekong Upstream = Chaktomuk Junction, Chaktomuk Junction = Mekong Downstream + Bassac. However, these calculations fall just outside the margin of error.

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