Mekong River Commission
Mekong River Commission
Ng H.H.,National University of Singapore |
Vidthayanon C.,Mekong River Commission
Zootaxa | Year: 2011
Pseudeutropius indigens, a new species of schilbeid catfish from peninsular Thailand, is described here. It can be distinguished from congeners in having the following combination of characters: head length 23.1-24.3% SL, head width 10.5-11.0% SL, length of anal-fin base 45.6-50.4% SL, 37-41 anal-fin rays, isognathous jaws in which the premaxillary teeth are not visible when the mouth is closed, and long nasal, maxillary and mandibular barbels that reach to at least the analfin origin. Copyright © 2011 Magnolia Press.
Kranz N.,Ecologic Institute Berlin |
Menniken T.,Mekong River Commission |
Hinkel J.,Potsdam Institute for Climate Impact Research
Environmental Science and Policy | Year: 2010
The challenge of governing transboundary water resources is expected to increase with climate change and the resulting need to adapt to its impacts such as temperature increase, more precipitation in the wet season and less in the dry season. In a number of transboundary basins, international regimes, and in particular river basin commissions, are emerging to account for this and other challenges. Some basins are, however, rather advanced in terms of developing climate change adaptation strategies, while others are in a more nascent stage. For the two case studies of the Orange-Senqu and Mekong river basins, this paper attempts to explain the different degrees of progress towards climate change adaptation by applying regime effectiveness analysis. First, we analyze, using the Activity Diagram (AD) of the Management and Transition Framework (MTF), at which stage in the climate change adaptation policy formation process each of the two basins is. Then we attempt to explain the different degrees of progress towards adaptation by means of regime effectiveness theory. Variables indicating regime effectiveness are taken from the literature and further developed to suit the context of climate change adaptation. We find that the different degrees of progress can partially be explained by some variables of regime effectiveness such as the characteristics of rules and procedures, organizational structure, the role of riparian countries as well as international context. At the same time, the analysis points to the need for an analysis of additional factors that potentially shape decision-making and policy processes for climate change adaptation in international river basins such as (a) the hydrological, political and socio-economic setting, (b) underlying principles of regional cooperation (or conflict), (c) interests and values of the various actors in the negotiation process and (d) the possible linkages and trade-offs with other policy fields. © 2010 Elsevier Ltd.
Liu J.,Innovation International |
Paul S.,Innovation International |
Manguerra H.,Mekong River Commission
Proceedings of the Watershed Management Symposium | Year: 2015
In 2012, the Halstead Fire of Salmon-Challis National Forest affected approximately 174,000 acres of forest. The post-fire logging operation to recover marketable timber calls for an extensive investigation on the environmental impacts of logging activity towards forestry recovery and sediment/water yields. This study analyzed these issues and compared seasonal differences between winter and summer logging using ArcSWAT. The 95,275-ac watershed was divided into three sub-watersheds for analyses. Corresponding to fire damage levels, land types of barren, grass, and shrub were assigned to areas with high, medium, and low severities, respectively. Four post-fire scenarios were simulated: No Recovery (NR)-the land type distribution remains unaltered after the fire and no logging activities; No Action (NA)-natural forestry recovery without logging operation; Summer Logging (SL)-forest recovery with logging activities in summer; Winter Logging (WL)-forest recovery with logging activities in winter. These scenarios were modeled using ArcSWAT from 2012 to 2019, with the proposed logging operation from 2015 till 2019. The outputs indicate that NR and SL can generate higher sediment/water yields; while NA and WL have similar yields as 95% of NR or SL. For the entire watershed, SL can generate 0.64 Mg/ha or 7,798 Mg/yr sediment more than WL, as well as 6,573 kg nitrogen and 1,827 kg phosphorus more than WL annually. The logging activities in winter after wildfires may be an optimal solution to recover marketable timber in terms of minimal environmental impacts towards the forestry ecosystem. © 2015 ASCE.
Trinh S.B.,University of East Anglia |
Trinh S.B.,Mekong River Commission |
Hiscock K.M.,University of East Anglia |
Reid B.J.,University of East Anglia
Environmental Pollution | Year: 2012
Mechanistic insights into the relative contribution of sorption and biodegradation on the removal of the herbicide isoproturon (IPU) are reported. 14C-radiorespirometry indicated very low levels of catabolic activity in IPU-undosed and IPU-dosed (0.1, 1, 100 μg L -1) river water (RW) and groundwater (GW) (mineralisation: <2%). In contrast, levels of catabolic activity in IPU-undosed and IPU-dosed river sediment (RS) were significantly higher (mineralisation: 14.5-36.9%). Levels of IPU catabolic competence showed a positive log-linear relationship (r 2 = 0.768) with IPU concentration present. A threshold IPU concentration of between 0.1 μg L -1 and 1 μg L -1 was required to significantly (p < 0.05) increase levels of catabolic activity. Given the EU Drinking Water Directive limit for a single pesticide in drinking water of <0.1 μg L -1 this result suggests that riverbed sediment infiltration is potentially an appropriate 'natural' means of improving water quality in terms of pesticide levels at concentrations that are in keeping with regulatory limits. © 2012 Elsevier Ltd. All rights reserved.
News Article | September 21, 2016
The Association of Southeast Asian Nations or ASEAN is a political and economic organization of ten Southeast Asian countries. Formed in 1967 by Indonesia, Malaysia, the Philippines, Singapore and Thailand, membership has since expanded to include Brunei, Cambodia, Laos, Myanmar and Vietnam. Covering more than 4.5 million square kilometres and comprising a population of more than 600 million people, ASEAN is about the size of the European Union. But these are probably the only similarities the two economic unions share. While the EU currently has more economic power, is further developed and is a politically more integrated trade zone, when it comes to GDP growth, ASEAN, is by far the more dynamic. For the last five years, its annual GDP growth averaged around 6%. In 2015, the organization’s combined nominal GDP had grown to more than USD 2.6 trillion. If ASEAN were a single entity, it would rank as the seventh largest economy in the world behind the USA, China, Japan, Germany, France and the United Kingdom. With such economic growth levels expected to continue, ASEAN is fast becoming a major economic force in Asia and a driver of global growth. Installing sufficient additional power generation capacity is one of the most pressing issues for ASEAN countries to solve. Despite the rapid economic development, many parts of ASEAN remain under-electrified – 160 million of its people still do not have access to electricity today. For those that do, prices of grid electricity are high at 0.18 USD/kWh or more in some markets. The insufficient power generation structures currently in ASEAN are characterized by their strong reliance on fossil sources, such as natural gas, coal and oil, and the absence of nuclear power. ASEAN, one of the regions with the strongest growth in CO2 emissions in the last decade is also the region expected to experience some of the most harmful effects of climate change – more intensive storms, variable precipitation, a rise in sea levels, as well as more severe droughts and floods. Like many emerging economies with sizeable populations, ASEAN must solve numerous economic and energy-related challenges such as providing sufficient energy services, improving industrial productivity and reducing poverty, and on top of that, adapting to global warming. As a result, ASEAN is increasingly turning to renewable energy. Rather than transitioning from existing, reliable and adequate fossil and nuclear energy infrastructure towards renewable energy sources driven by political will, as we have experienced in developed markets such as Europe, the USA or Japan, renewable investments in Southeast Asia are first and foremost driven by the need to increase energy capacity. Happily, ASEAN’s excellent natural resources coincide very well with this objective. The most common and historically advanced form of renewable energy has been hydroelectric power, most prominently from the lower Mekong River, which flows through or along the borders of Myanmar, Laos, Thailand, Cambodia and Vietnam. In the lower Mekong, more than 3,235 MW has been realized via facilities built largely over the past ten years, while projects under construction represent an additional 3,209 MW. A further 134 projects are planned for the lower Mekong, which will effectively exhaust the river’s hydropower generating capacity. Although the overall hydroelectric potential of the region is estimated to range between 170 GW to 250 GW, there are major concerns about the environmental impacts of damming the Mekong River system and other rivers in Southeast Asia. An independent assessment prepared for the Mekong River Commission recommended a 10 year delay in the current hydroelectric project schedule to evaluate environmental concerns. The highest geothermal potential of any country in the world with more than 27 GW is in Indonesia. Although ASEAN’s largest country by population and economy contains 40 percent of the world’s total geothermal reserves, it currently only utilizes five percent of its capacity. The Indonesian government plans to increase its geothermal capacity to 6 GW by the end of this decade. So far its main challenge has been to attract the necessary foreign investment. The current administration has undertaken some reforms to lower the barriers in this regard with the result that Enel Green Power has recently kicked off its 55 MW geothermal development in Indonesia’s Lampung province. Wind energy has been a relatively low priority renewable sector in ASEAN. The region’s best wind potential lies to the north in areas of Vietnam, Cambodia, Myanmar, Laos, the Philippines and Thailand. Of these, only the Philippines, Thailand, and Vietnam have started to substantially promote the wind energy sector. The Philippines now has an operational wind energy capacity of 400 MW, more than any other country in Southeast Asia. Vietnam recently – after years of delay – moved towards its announced wind power targets with the long awaited corresponding FiT programme. During the visit of President Obama to Vietnam in May 2016, a MoU with General Electric was signed according to which GE will develop new wind farms with a capacity of 1,000 MW. The largest and most unlimited potential for ASEAN in renewable energy is, of course, in solar power. With annual solar radiation levels ranging from 1,460 to 1,900 kWh/m2 per year the region has some of the highest yields in the world. Thailand began substantial implementation of solar power in 2011 with an attractive subsidy “adder” scheme. Although Thailand with about 2.8 GW has currently more solar power cumulatively installed than all other ASEAN countries combined, the Philippines is expected to install the most PV this year. However, while Thailand and the Philippines dominate the market today, in just a few short years, four countries will be competing for the title of the top PV market in ASEAN’s rapidly growing solar power region, as can be seen in Figure 1. Next to Thailand, Malaysia has run a smaller FiT programme for solar in the last few years amounting to a little over 220 MW up to now and has announced another 250 MW to be approved for FiT by 2020. Most dynamic in the last year, however, has been the Philippine solar market. Driven by attractive FiTs, 800 MW has been built – more than 90% of that within the very short timeframe of Q4/2015 and Q1/2016. Most interestingly, the FiT for projects had to be secured after the project’s commissioning and was subject to a 450 MW cap for the FiT capacity. This highlights another big difference to developed RE markets such as Europe and the USA: The market demand for PV is not dependent on a subsidized FiT in the Philippines because of growing demand for electricity and PV’s cost competitiveness compared with other power generation options. Project financing has been available in spite of the uncertainty about securing a FiT. In recent months, both Vietnam and Indonesia announced the implementation of FiT programmes for 850 MW and 250 MW of solar respectively, which are to be carried out in 2016/2017. Another substantial difference to a developed solar market in for example, Europe or the USA, is the market segmentation. While residential solar represents a substantial share of solar power capacity in markets such as Germany or the USA, in ASEAN, utility-scale solar accounts for more than 95% of all capacity added to the grid. Although there are some initiatives and programmes to enhance residential solar, the market fundamentals of often state-owned national utilities are in favour of utility-scale solar applications in the medium-voltage power-grid sector. Residential has been slow to take off due to low household income, lack of necessary financing for residential applications and shorter amortization expectations by ASEAN customers. Thailand, the Philippines, Indonesia, Malaysia and Vietnam are the five big markets driving PV development. Combined, they represent 90% of ASEAN’s population; they have capacity targets in place and policies taking shape. According to Apricum’s high case scenario, over 15 GW of PV capacity is expected to be installed by 2020 in these five markets alone, as illustrated in Figure 2. FiTs in markets such as Thailand and the Philippines have succeeded in opening up the market for solar in these countries, however, as these programmes come to an end, market players must turn towards the natural potential and economics of utility-scale power provision to utilities or commercial off-takers. Utility-scale solar will compete with mostly LNG or other renewables, rather than with new coal power plants, which face strong public opposition and have recently been subject to political scrutiny. On the commercial off-taker side, renewable generation will need to be competitive with electricity prices (including those that are subsidized in some markets such as Indonesia and Vietnam) from the utility at peak-times or diesel generation on site – which it already is in most cases. In contrast to Asian markets such as China or Japan, ASEAN features much lower market-entry barriers for international renewable energy developers, EPCs or financiers. Solar markets such as Thailand and the Philippines demonstrate an almost equal balance between competent new local players and international renewable energy companies who are establishing a strong, even leading, market position. There are several key factors that are vital for international renewable energy companies wishing to successfully build a business in Southeast Asia. These include time-to-market, the level of commitment to the market, the in-depth understanding of the regulatory environment, the local organizational execution capabilities and the level of integration of the business model. Apricum offers a range of services to support companies in successfully expanding into new markets. These include identifying the most promising markets, developing business models and entry strategies tailored to these specific markets, finding the most suitable local partners and facilitating local business development. Buy a cool T-shirt or mug in the CleanTechnica store! Keep up to date with all the hottest cleantech news by subscribing to our (free) cleantech daily newsletter or weekly newsletter, or keep an eye on sector-specific news by getting our (also free) solar energy newsletter, electric vehicle newsletter, or wind energy newsletter.
News Article | November 10, 2016
The VMod hydrological model32 was selected on the basis of its success in previous studies of the Mekong River basin26, 33, 34. As implemented for the Mekong River, VMod employs a 5 km × 5 km (25 km2) grid, with surface elevation, gradient, aspect, vegetation and soil type in each cell being extracted from the SRTM DEM35, GLC2000 land cover36 and FAO soil type37 data sets, respectively. VMod simulations were forced using daily rainfall and temperature data estimated from a network of 151 meteorological stations (Fig. 1a). Specifically, the precipitation data employed here are from the Mekong River Commission (MRC) hydrometeorological database38, supplemented with Global Surface Summary of the Day (GSOD) data39 for the Chinese part of the basin. These data have been carefully quality controlled33, and the MRC data therefore represent the highest-quality available data, with the best density of precipitation stations. However, as is frequently the case in developing nations, resource constraints have meant that there has not yet been a more recent release of the MRC product, constraining our study to the period 1981–2005. However, also pertinent to this choice of study period is the fact that in 2005 the total active storage of all dams on the Mekong was 7.2 km3, of which the active storage of Chinese dams was only 0.8 km3, meaning that the potential impact of dams is still rather minor at this date8. In contrast, by the year 2015 these figures had increased to ~55 km3 and 24 km3, respectively. Estimates of daily rainfall totals and temperatures within each VMod grid cell were obtained by interpolating from the three nearest observations using inverse distance squared weighting. For daily rainfall totals, a multiplicative elevation correction (with coefficient 0.0002 mm m−1) was employed to account for differences of elevation between each observation point and the location of the grid cell, whereas the temperature data were corrected for elevation using a lapse rate of −0.006 K m−1. VMod simulates snowmelt using a degree-day model, in which the amount of snowmelt is obtained from daily average temperature exceeding a given threshold multiplied by a snowmelt coefficient K . The model also computes snow evaporation, snowpack water storage, and refreezing. The snowmelt parameters employed here were calibrated in a previous study33 using flow measurements at the Chiang Saeng gauging station. Glacier melt is computed similarly to snowmelt, albeit using a different set of parameters and the assumption of infinite storage. In VMod the flow discharge is routed along a river network that is generated using DEM and map data. Each model grid cell has a river, either starting at that grid cell or flowing through it, to which the runoff from the cell is added. Flow within the river network is computed using a one-dimensional river model with a kinematic wave approximation. In this way simulated runoff at any point in the network reflects both the local and upstream contributions of precipitation, with the precipitation being deconstructed into cyclone and non-cyclone components as described later. In this approach the flow discharge (and hence sediment transport) that is attributable to cyclone and non-cyclone rainfall components at a given location in the river network is not explicitly parsed out as being attributable to a specific rainfall event. Instead, the simulated runoff components reflect the integrated effects of series of rainfall events that are delivered over longer time periods. Note that in the flow routing process, river cross-sections are represented using two superimposed trapezoids, with the lower one representing the main channel and the upper the floodplain, allowing for a representation of the effects of overbank storage on downstream attenuation of the flood wave. Figure 2 (for Kratie, along with the left-hand panels of Extended Data Fig. 3 for the other hydrological stations) shows a comparison of simulated VMod versus observed runoff regimes at each of the gauging stations employed in this study. Note that, for clarity, Fig. 2 shows data only for the period 1995–2000, a period that includes the years that are most and least affected by TCs, but the goodness-of-fit measures reported here are for the entire simulation period (1 May 1981 to 31 March 2005). The four goodness-of-fit measures used are: (1) the mean discrepancy ratio for daily flows (Me), which is the average of all the ratios (computed at each daily time step) of simulated to observed daily water flows, with Me = 1 indicating perfect agreement between simulated and observed data; (2) the mean discrepancy ratio for annual peak flows (Me ); (3) the root mean square error (r.m.s.e.); and (4) the Nash–Sutcliffe Index (NSI)40. On the basis of these metrics (Extended Data Fig. 3), VMod, on average, under-predicts daily water flows throughout the study reach, while under-predicting the annual flood maxima in the lower parts (Stung Treng and Kratie) and over-predicting annual flood maxima in the upper parts (Luang Prabang, Mukdahan and Pakse) of the reach (Fig. 2 and Extended Data Fig. 3). Nevertheless, with NSI values varying between 0.749 (Luang Prabang) and 0.922 (Pakse), the overall performance of VMod is either ‘very good’ (Luang Prabang, Mukdahan, Stung Treng) or ‘excellent’ (Pakse, Kratie), on the basis of the classification scheme of Henriksen et al.41 The hydrological model as described earlier was run with two rainfall scenarios. The first ‘baseline’ scenario replicated actual conditions in the 1981–2005 study period and employed observed rainfall totals. In the second scenario, these baseline totals were revised downwards by removing the rainfall estimated to have been delivered by tropical cyclones. The simulated runoff associated with tropical cyclones (Q ) was then computed by differencing the daily flows simulated under the two scenarios. To estimate rainfall totals associated with TCs, we first employed the IBTrACS (version v03r02) storm tracks database42 to locate the paths, at daily time steps, of all recorded TCs intersecting or passing near the Mekong Basin during 1981–2005. Rainfall anomalies associated with these storm paths were then defined by first interpolating, using the nearest neighbour, daily rainfall values observed at the network of 151 stations used in the baseline rainfall scenario onto a 0.1° (~11 km2) resolution grid. Next, all rainfall stations located within a 500 km Haversine search radius43, 44 from the centroid of the storm on that date were identified. These identified stations were then temporarily (for the specific time step) removed from the analysis and an updated rainfall surface (minus the identified stations) was re-interpolated onto the same 0.1° grid. A rainfall anomaly surface, representing estimated rainfall associated with the identified storm and time step, was obtained by differencing the original and updated surfaces. This process was repeated for each daily time step, allowing the observed rainfall series at each meteorological station to be adjusted by subtracting rainfall anomalies within the grid square specific to each gauge from the observed daily rainfall totals. Note that since the hydrometeorological database we used in this analysis does not discriminate between precipitation associated or not associated with TCs, it is not possible to validate our estimates of cyclone-derived precipitation. For this reason, our estimates of rainfall associated with TCs are deliberately based on a method (nearest neighbour interpolation) that is more conservative than previous studies44, which simply assume that all rainfall within the assigned search radius is related to tropical cyclones. By the same token, while acknowledging that there is uncertainty regarding the typical radii of tropical cyclones, our decision to employ a 500 km search radius is again conservative in that it is at the lower end of the range of values typically used in previous studies45. The IBTrACS data on which the above analyses are founded comprise six hourly best-track positions and intensity estimates. Only storms designated as in a tropical phase with one-minute maximum sustained surface wind speeds exceeding 34 knots (17.5 m s−1) are included in our analysis. The IBTrACS data were also used to compute the accumulated cyclone energy (ACE) metric46 that we employ to characterize the TC climatology over the Mekong River basin for the period 1981–2005. The ACE parameter is analogous to the power dissipation index (PDI)47 in that it convolves intensity and duration information for each individual TC observed in a defined area (here the sub-basins for the five gauging stations that are the focus of this study), offering considerable advantages over definitions based on the more familiar categorizations on the basis of wind speed48. In this context, our estimates of ACE are obtained by squaring the 6-hourly intensity estimates reported in the best-track database and integrating over the 1981–2005 study period. were constructed for each hydrological station on the Mekong River mainstem below the China–Laos border and upstream of the Mekong Delta by fitting observed suspended solids concentration (SSC; C) and observed water discharge (Q) data (Extended Data Table 3) using nonlinear estimation techniques constructed using the Curve Fitting Toolbox in Matlab version R2014a. Specifically, a nonlinear least-squares power-law solver with one term was applied to the raw data, using the Trust-Region algorithm. The use of the power-law solver follows previous work49, 50, 51 in optimizing the fit at the higher values of discharge and concentration that dominate overall transport. This procedure results in a poor fit for low discharges at Pakse (Extended Data Fig. 2) but using an alternative solver, designed to improve the low fit, is not justified. This is because doing so makes only a very minor (<2%) difference in the mean annual sediment load at Pakse while introducing substantial errors into the more important high-flow fits at the other stations. Note that our focus on suspended, rather than total, sediment load is not problematic since bed load is less than 20% of the total load (on the basis of comparisons of rivers from the data compilation of Turowski et al.52 with suspended sediment concentrations similar to those of the Mekong River). In terms of the data sources feeding into the sediment rating curves (Extended Data Table 3), at Luang Prabang, Mukdahan and Pakse the SSC and water discharge data were obtained from hydrological records archived by the MRC (available to download from http://portal.mrcmekong.org/index). However, the MRC SSC measurements are available only sporadically and have been acquired using a range of methodologies (reflecting the different approaches taken by differing hydrological agencies in this trans-national river) at the different gauging stations (Extended Data Table 3). All of the MRC’s SSC measurements at Mukdahan were collected using United States Geological Survey (USGS)-designed isokinetic depth-integrated samplers (USGS D49 samplers) deployed at three verticals over the cross-section. The three samples are composited to provide a single sample from which the SSC is determined50. For the stations in Laos (that is, Luang Prabang and Pakse), the MRC SSC data were initially (1961) collected for a brief period using the same procedures as at Mukdahan, but subsequently the depth-integrated samplers were replaced with USGS P61 point-integrating samplers. To avoid potential problems with mixed sampling protocols in the data sets, and because depth-integrated sampling relies heavily on the even ascent of the sampler through the water column, to avoid biasing the SSC we excluded the relatively few data obtained using depth-integrated samplers from further consideration. The point-integrated samplers were deployed at three verticals over the cross-section, at heights of 0.2, 0.5 and 0.8 of the flow depth in the case of the point-sample (producing nine individual samples, from which the mean SSC for the cross-section is obtained by simple averaging). However, as shown in Extended Data Fig. 7, because the concentration of suspended sediment varies, both through the water column and laterally over the cross-section, simple averaging of point-based samples systematically biases the resulting estimate of the cross-section averaged SSC (relative to that obtained from alternative quasi-synoptic sampling techniques). We corrected for this effect by reducing the SSC values recorded within the MRC database by 26% for all the Laos and Thai stations (Extended Data Fig. 7). We derived this correction factor by comparing the averaged cross-section SSC computed from acoustic Doppler current profiler (aDcp) surveys in Cambodia, these aDcp surveys being undertaken as part of an aDcp field calibration exercise designed to retrieve SSC data from aDcp records archived by the Cambodian hydrological agency. For the stations at Stung Treng and Kratie, sediment rating curves were constructed using flow discharge and SSC data (Extended Data Table 3) retrieved from the archives of the Cambodian Department of Hydrology and Water Resources (DHRW). These DHRW data were acquired via deployments of a four-beam 600 kHz aDcp (RD Instruments) during routine surveys undertaken in the period 2009 to 2014 by DHRW personnel. These aDcp surveys do not directly record suspended solids concentrations, but rather the archived DHRW data files contain acoustic backscatter (ABS) information recorded during the original surveys. We retrieved SSCs from these ABS data by means of a calibration function (Extended Data Fig. 7) that we derived on the basis of 54-point measurements of SSC deployed contemporaneously with the DHRW aDcp to record coeval ABS values in the same parcel of water following past guidelines53, 54, 55. In this field calibration procedure, the SSC data were obtained by filtering (Whatman GF/C glass microfibre grade 47 mm diameter 1.2 μm filter paper) and weighing the mass of solids retained from water samples collected at a wide range of flow depths and channel locations using a 3 l Van Dorn sampler56 during fieldwork that was spread over a wide range of flow conditions during 2013 and 2014. Consequently, the calibration function encompasses a wide range of SSC and ABS data. Analysis of ABS values and the suspended sediment grain size collected from the point samples reveals there is no relationship between the two, probably owing to the narrow range of grain sizes within the LMR57. Since the aDcp data provide a quasi-synoptic (less a blanking zone of 0.5 m at the top of the water column and a side-lobe interference zone of 10% of the flow depth at the bottom of the water column) image of ABS over the channel cross-section, the calibration function can be used to transform the ABS data to an accurate estimate of section-averaged SSC (Extended Data Fig. 7), as also noted earlier. Having derived the rating curves for each gauging station (Extended Data Fig. 2), we then explicitly investigated whether the rating curves exhibit hysteresis effects associated with sediment exhaustion, which might be expected to lead to lower SSC values for a given discharge on the falling versus rising stages of the annual flood wave. However, no such evidence of hysteresis was identified (see Extended Data Fig. 2), presumably as a result of fluctuations in SSC being subdued owing to the large catchment areas and consequent effects of channel and floodplain storage in attenuating the peaks25. We also considered whether there is a shift in sediment transport during flows affected by TCs, for example as a result of increased sediment supply from catchment erosion during storms. Specifically, we evaluated whether there are differences in sediment rating curves for flows that are (using the VMod model outputs to identify TC-affected flows and then cross-matching to identify SSC measurements that are TC affected) or are not affected by TCs. As indicated in Extended Data Table 3, this enabled us to identify 34 SSC samples during TC affected flows at Luang Prabang (14% of all observations at that station), while 30 samples were identified during TC affected flows at Mukdahan (3% of observations). We found that there were no significant (analysis of variance (ANOVA), P > 0.05) differences between sediment ratings developed using the TC-affected versus the non-TC affected SSC data at either station. This indicates that we can with confidence apply single rating curves for these stations, for both TC-affected and TC-unaffected flows. Since we are only able to discriminate TC-affected flows from VMod outputs during the 1981–2005 study period, and because there are no SSC data from this period at Stung Treng and Kratie, and there are too few SSC data at Pakse to identify any TC-affected measurements, there are no data to complete a similar formal analysis at these other three stations (Extended Data Table 3). Nevertheless, the very tight fit of these three stations’ ratings (Extended Data Fig. 2), alongside the point that these stations are TC-affected during the period of SSC data collection, indicates that any shift in sediment transport processes during TCs is unlikely to have any material effects on the estimation of suspended solids loads at these locations. Bearing in mind the relatively long periods over which the SSC data used to construct the sediment ratings at Luang Prabang, Mukdahan and Pakse were collected (Extended Data Table 3), we also tested for the possibility that varying ENSO phase, a known cause of hydroclimatological variability in the Mekong River, may lead to non-stationarity in the SSC values at these stations58, 59, using dummy-variable regression analysis. Letting Z = 1 if ENSO phase is positive (that is, El Niño) and 0 otherwise, then for the slope of the regression: Then, for the intercept of the regression: Or, for both slope and intercept: We found no significant difference at the 0.05 significance level (ANOVA on dummy-variable regression coefficients for each site) in the SSCs, for a given Q, as a function of ENSO phase, demonstrating that there is therefore no evident bias in the SSCs introduced as a function of climate variability associated with ENSO. With the completion of the first major main-stem cascade of dams on the Chinese portion of the Mekong River in 1993, we also considered whether the SSC data differ pre- and post-1993. Accordingly, a similar analysis (equations (3) to (8)) was conducted for those sites (Luang Prabang and Mukdahan) at which SSC samples span the pre- and post-dam periods. We found that at Mukdahan no significant difference exists at the 0.05 significance level (ANOVA on dummy-variable regression coefficients), implying that there is no reason to split the data based on the pre- and post-dam periods. However, a significant difference (P < 0.05) between the pre- and post-dam periods does exist at Luang Prabang (ANOVA test statistic = 9.7377, n = 236, degrees of freedom (df) = 1,232). Consequently, at Luang Prabang, we calculate suspended solids loads (see later) using the pre- and post-dam rating curves (Extended Data Fig. 2) for the periods 1981–1992 and 1993–2005, respectively. Finally, we emphasize that our analysis does not account for anthropogenic factors, such as flow regulation through reservoirs, land-use or land cover change, or increasing sediment mining, which could potentially introduce a trend into the relationships between flow discharge and SSC at each gauging station. Our suspended sediment rating curves therefore assume stationarity of these factors over the 1981–2005 study period. The lack of hysteresis and apparent stationarity of the SSC data means that we were able to employ a single (two at Luang Prabang, one for the pre- and one for the post-dam periods) sediment rating curve specific to each station (Extended Data Fig. 2), together with the continuous water discharge records obtained from our hydrological modelling, to estimate daily suspended solids loads (Fig. 2 and Extended Data Fig. 3) for the 1981–2005 study period. These daily loads were in turn used to compute, by summation, the annual sediment loads for each station (Fig. 3 and Extended Data Fig. 4). Note that since the modelling period extended from 1 May 1981 to 31 March 2005, we report annual sediment loads only for those years (1982 to 2004 inclusive) for which full-year records are available. Mean annual suspended solids loads for each station over the 22-year period (1982 to 2004) were then obtained by calculating the arithmetic mean of these annual loads (Extended Data Table 2). No statistical methods were used to predetermine sample size. Mann–Kendall60 tests, used to evaluate whether there are significant (at 95% confidence) temporal trends (the magnitude of the trend being equated to Sen’s slope, with uncertainty equated to the 95% confidence bounds on the Sen slope estimates) in the computed annual sediment loads, were computed in Matlab R2014a using the ktaub.m file written by J. Burkey (2006), which is available from the Matlab Exchange (http://www.mathworks.com/matlabcentral/fileexchange/11190-mann-kendall-tau-b-with-sen-s-method–enhanced-/content/ktaub.m). The precipitation and temperature data used in the hydrological model simulations are taken from the MRC hydrometeorological database38 (not available online) supplemented with GSOD data39 for the Chinese part of the basin (ftp://ftp.ncdc.noaa.gov/pub/data/gsod/; years 1981–2005). The IBTrACS (version v03r02) storm tracks database42 that we used to estimate the track locations and hence precipitation anomalies associated with TCs was downloaded from the IBTrACS website (https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data; IBTrACS-All data v03r02 all storms line shapefile). Note that we are not able to make the input data files used in the hydrological model simulations available as the precipitation and temperature data are from the MRC (as described earlier) under a licence that precludes redistribution of products or derived products. Water discharge data used in the validation of the hydrological model are from the hydrological records archived in the MRC data portal (http://portal.mrcmekong.org/index as discharge records from Luang Prabang (station identifier 011201; unique data set accession 21301), Mukdahan (station identifier 013402; unique data set accession 3301), Pakse (station identifier 013901; unique data set accession 3141), Stung Treng (station identifier 014501; unique data set accession 2809), and Kratie (station identifier 014901; unique data set accession 2811)), as are the suspended sediment concentration data (available from http://portal.mrcmekong.org/index as sediment concentration records from: station identifier 011201, unique data set accession 4746; station identifier 013402, unique data set accession 4849; and station identifier 013901, unique data set accession 4773, respectively) used to derive the sediment rating curves at Luang Prabang, Mukdahan and Pakse. The aDcp data files used to derive the sediment rating curves for the stations at Stung Treng and Kratie are available on request from the Cambodian Department of Hydrology and River Works (DHRW; http://www.dhrw-cam.org/index.php). The VMod hydrological model software as employed in this study is available to download from http://www.eia.fi/vmod.The related analytical code comprises the bespoke Matlab scripts, authored by J.L., that were used to partition out the cyclone-influenced rainfall. These scripts are not publically available as they are currently being developed and used in commercial applications.
News Article | April 5, 2016
Research commissioned by Vietnam has warned of devastating environmental and economic effects for millions of people living along the Mekong River if 11 proposed dams are built on its mainstream. The 2 1/2-year study by Danish water expert DHI was submitted recently by Vietnam to the Mekong River Commission, a body comprising Thailand, Vietnam, Cambodia and Laos that was set up to mediate the conflicting water priorities of Mekong countries. The commission released a five-page summary of the study to The Associated Press on Tuesday. It predicts "high to very high adverse effects" on fisheries and agriculture in Cambodia and Vietnam if all 11 dams are built, and even greater damage if the Mekong's tributaries also are dammed. The famed Irrawaddy dolphin would likely disappear from the Mekong, it says. Unmitigated hydropower development will cause "long-lasting damage to the floodplains and aquatic environment, resulting in significant reduction in the socio-economic status of millions of residents," according to the study. Much of Southeast Asia is suffering a record drought due to El Nino, and officials in Vietnam have said the effects are exacerbated by existing Chinese dams on the upper Mekong. The rice-bowl-sustaining river system flows into Myanmar, Laos, Thailand, Cambodia and Vietnam. The Mekong is also one of the world's largest inland fisheries, providing a livelihood to millions of people. Dams diminish fishing grounds by creating barriers to breeding-cycle migrations and creating river conditions that destroy habitat and food sources. The study said agricultural production in the lower reaches of the Mekong Delta would drop steeply because the dams would trap river sediments, resulting in large reductions in the volume of nutrients flowing downstream. Less sediment downstream would also make the delta more at risk of saltwater incursion that can render land infertile. It predicts annual fishery and farming losses of more than $760 million in Vietnam and $450 million in Cambodia. Fish catches would drop by 50 percent for Vietnam and Cambodia, and 10 percent of the delta's fish species would either disappear from the region or become extinct. The incomes of fishing and farming villages would likely fall by half. Laos is behind many of the new dams proposed for the lower Mekong and went ahead with construction of the Xayaburi dam in 2012 despite the concerns of neighboring countries. It wants hydropower exports to become a mainstay of its economy, which is among the least developed in Asia. The river commission said the Vietnamese report will help its own study, which was commissioned in 2011 and is expected to be completed next year.
Ng H.H.,Lee Kong Chian Natural History Museum |
Vidthayanon C.,Mekong River Commission
Zootaxa | Year: 2014
We review members of the sisorid catfish genus Exostoma known from Thailand. Three species are recognized, of which two from the headwaters of the Chao Phraya River drainage in northwestern Thailand, are described here as new: E. effrenum and E. peregrinator. In addition to the two new species, E. berdmorei (which is here redescribed) is also known from the Salween River drainage in western Thailand. The three species can be distinguished from each other and other congeners by the morphologies of the adipose and caudal fins, as well as morphometric data for the eye diameter, head width, dorsal-to-adipose distance, body depth at anus, caudal-peduncle length, caudal-peduncle depth, and numbers of branched pectoral-fin rays and preanal vertebrae. Copyright © 2014 Magnolia Press.
Arias M.E.,University of Canterbury |
Cochrane T.A.,University of Canterbury |
Kummu M.,Aalto University |
Holtgrieve G.W.,University of Washington |
And 2 more authors.
Ecological Modelling | Year: 2014
The Tonle Sap is the largest lake in Southeast Asia and its fishery supports the livelihood and nutrition of millions of people in Cambodia. However, the hydrological and ecological drivers of this ecosystem are changing as a result of hydropower development on the Mekong River and global climate change. The objective of this study was to quantify the impacts of the Mekong's future hydrological alterations on aquatic net primary production (NPP) of the Tonle Sap. A three-dimensional (3D) hydrodynamic model was used to evaluate eleven scenarios of hydropower development and climate change, with respect to water flows, suspended sediments, and floodplain habitat cover, which were identified as the key drivers of productivity change. We found that hydropower development would cause the most distinct changes in seasonality by reducing wet season water levels and increasing dry season water levels. Combined scenarios of hydropower and climate change revealed that areas of open water and rainfed/irrigated rice would expand by 35. ±. 3% and 16. ±. 5%, respectively, while optimal area for gallery forest would decrease by 40. ±. 27%. The estimated annual net sedimentation was projected to decrease by 56. ±. 3% from the 3.28. ±. 0.93 million tons baseline values. Annual average NPP in the open water and in the floodplain was 1.07. ±. 0.06 and 3.67. ±. 0.61 million tons C, respectively, and a reduction of 34. ±. 4% is expected. Our study concludes that Tonle Sap's drivers of ecological productivity - habitat cover, sedimentation, and NPP - will face a significant change, and a decline of its ecosystem's services should be expected if mitigation and adaptation strategies are not implemented. © 2013 Elsevier B.V.
Nguyen P.K.-T.,Nanyang Technological University |
Chua L.H.-C.,Nanyang Technological University |
Son L.H.,Mekong River Commission
Natural Hazards | Year: 2014
Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m. © 2013 Springer Science+Business Media Dordrecht.