The world's large rivers are facing reduced sediment loads due to anthropogenic activities such as hydropower development and sediment extraction. Globally, estimates of sand extraction from large river systems are lacking, in part due to the pervasive and distributed nature of extraction processes. For the Mekong River, the widely assumed estimate of basin-wide sand extraction is 50 Mt per year. This figure is based on 2013 estimates and is likely to be outdated. Here, we demonstrate the ability of high-resolution satellite imagery to map, monitor, and estimate volumes of sand extraction on the Lower Mekong River in Cambodia. We use monthly composite images from PlanetScope imagery (5 m resolution) to estimate sand extraction volumes over the period 2016–2020 through tracking sand barges. We show that rates of extraction have increased on a yearly basis from 24 Mt (17 to 32 Mt) in 2016 to 59 Mt (41 to 75 Mt) in 2020 at a rate of ∼8 Mt yr⁻¹ (6 to 10 Mt yr⁻¹), where values in parentheses relate to lower and upper error bounds, respectively. Our revised estimates for 2020 (59 Mt) are nearly 2 times greater than previous best estimates for sand extraction for Cambodia (32 Mt) and greater than current best estimates for the entire Mekong Basin (50 Mt). We show that over the 5-year period, only 2 months have seen positive (supply exceeds extraction) sand budgets under mean scenarios (5 months under the scenarios with the greatest natural sand supply). We demonstrate that this net negative sand budget is driving major reach-wide bed incision with a median rate of −0.26 m a⁻¹ over the period 2013 to 2019. The use of satellite imagery to monitor sand mining activities provides a low-cost means to generate up-to-date, robust estimates of sand extraction in the world's large rivers that are needed to underpin sustainable management plans of the global sand commons.

Water infrastructure development is considered necessary to drive economic growth in the Mekong region of mainland Southeast Asia. Yet the current understanding of hydrological and flood pattern changes associated with infrastructural development still contains several knowledge gaps, such as the interactions between multiple drivers, which may have serious implications for water management, agricultural production, and ecosystem services. This research attempts to conduct a cumulative assessment of basin-wide hydropower dam construction and irrigation expansion, as well as climate change, implications on discharge, and flood changes in the Cambodian Mekong floodplain. These floodplains offer important livelihoods for a considerable part of the 6.4 million people living on them, as they are among the most productive ecosystems in the world – driven by the annual flood pulse. To assess the potential future impacts, we used an innovative combination of three models: Mekong basin-wide distributed hydrological model IWRM-VMod, with the Mekong delta 1D flood propagation model MIKE-11 and 2D flood duration and extent model IWRM-Sub enabling detail floodplain modelling. We then ran scenarios to approximate possible conditions expected by around 2050. Our results show that the monthly and seasonal hydrological regimes (discharges, water levels, and flood dynamics) will be subject to substantial alterations under future development scenarios. Projected climate change impacts are expected to decrease dry season flows and increase wet season flows, which is in opposition to the expected alterations under development scenarios that consider both hydropower and irrigation. The likely impact of decreasing water discharge in the early wet season (up to −30 %) will pose a critical challenge to rice production, whereas the likely increase in water discharge in the mid-dry season (up to +140 %) indicates improved water availability for coping with drought stresses and sustaining environmental flows. At the same time, these changes would have drastic impacts on total flood extent, which is projected to decline by around 20 %, having potentially negative impacts on floodplain productivity and aquaculture, whilst reducing the flood risk to more densely populated areas. Our findings demonstrate the substantial changes that planned infrastructural development will have on the area, potentially impacting important ecosystems and people's livelihoods, calling for actions to mitigate these changes as well as planning potential adaptation strategies.

Recently, groundwater sources are being polluted by various activities such as agriculture, livestock, decentralized wastewater treatment systems and acid rain. Groundwater can also be polluted by landfill leachate, sewage, mine tailings, non-engineered deep well disposal of liquid waste and seepage from industrial waste lagoons. There are many studies reported contaminated groundwater remediation using Permeable Reactive Barrier systems (PRBs) and many countries happen to use this system to eliminate groundwater contaminants. This study reports the outcomes of the batch and the column test experiments conducted to evaluate the removal efficiency of four heavy metals: Cd(II), Cu(II), Fe(II) and Pb(II) using five locally available reactive materials (in Sri Lanka) with grain sizes less than or equal to 2.0 mm: Red Soil (RS), Laterite Soil (LS), Bangadeniya Soil (BS), Burnt Clay Tile (BCT) and Coconut Shell Biochar (CSB) as PRB materials. Seven columns (A-G) were filled with the reactive material of CSB (column E) and reactive material mixtures; RS + CSB (column A), LS + CSB (column B), BCT+CSB (column C), BS + CSB (column F), RS + LS (column G), with a weight ratio of 50:50 and RS + BCT + CSB (column D) with a weight ratio of 100/3:100/3:100/3. The results showed that the reactive materials filled in column A, B, D, F and G removed the metal concentrations, with a removal efficiency of over 90%, except Cd in the column with BCT + CSB mixture. Considering both the removal efficiency and the hydraulic conductivity of the columns, materials in column A, B, D and F are more effective than the others as PRB adsorbents for heavy metal remediation, while columns C and E have lower removal efficiency.

The main objective of this research is to regionalize the sediment rating curve (SRC) for subsequent sediment yield prediction in ungauged catchments (UCs) in the Lower Mekong Basin. Firstly, a power function-based SRC was fitted for 17 catchments located in different parts of the basin. According to physical characteristics of the fitted SRCs, the sediment amount observed at the catchment outlets is mainly transported by several events. This also indicates that clockwise hysteretic phenomenon of sediment transport is rather important in this basin. Secondly, after discarding two outlier catchments due to data uncertainty, the remaining 15 catchments were accounted for the assessment of model performance in UCs by means of jack-knife procedure. The model regionalization was conducted using spatial proximity approach. As a result of comparative study, the spatial proximity approach based on single donor catchment provides a better regionalization solution than the one based on multiple donor catchments. By considering the ideal alternative, a satisfactory result was obtained in almost all the modeled catchments. Finally, a regional model which is a combination of the 15 locally fitted SRCs was established for use in the basin. The model users can check the probability that the prediction results are satisfactory using the designed probability curve.

The applicability of multivariate interpolation and information entropy to optimize the raingauge network in the Mekong River Basin (MRB) is investigated. Three different spatial interpolation methods are tested: inverse distance squared (IDS), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). The validated results confirm that the GIDS method outperformed IDS and OK. The application of information entropy together with GIDS on a network of 57 gauges provided the same information content (7.34 nat) as could be obtained using all 6788 gauges in the MRB. Combining this result with meteorological and hydrological indicators revealed that the number of gauges for the optimum raingauge network could be reduced to 40. The results imply good applicability of the proposed method, which may be used to help prioritize efforts and funds to maintain the raingauge network in a given river basin.