Abstract. 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 This figure is based on 2013 estimates and is likely to be outdated.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.
Abstract. 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 is lacking, in part due to the pervasive and distributed nature of extraction processes. In the Mekong River, current basin wide estimates of sand extraction are 50 Mt, and based on estimates from 2013. 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 and show that rates of extraction have increased year on year from 24 Mt (17 Mt to 32) in 2016, to 59 Mt (41 Mt to 75 Mt) in 2020 at a rate of ~8 Mt yr−1 (6 Mt yr−1 to 10 Mt yr−1); where values in parenthesis relate to lower and upper error bounds, respectively. Our revised estimates for 2020 (59 Mt) are nearly two 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 five year period, only two months have seen positive (supply exceeds extraction) sand budgets under mean and upper bound scenarios (five months under the lower bound estimates). We demonstrate that this net negative sand budget to the river is driving major 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 provide a low-cost means to generate up-to-date, robust estimates of sand extraction in the worlds large rivers that are needed to underpin sustainable management plans of the global sand commons.
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.
Located in Southeast Asia, Cambodia is one of the most disaster prone countries, where flooding rank the top of the natural disaster. Flood affects and threatens not only humans' and animal's life, properties, infrastructures, but it is also an obstacle to the current development. Furthermore, without having the efficient modern technology to predict flood situation in Cambodia, the disaster in this country become more serious. The objective of this research study is to simulate flood inundation area by using software HEC-RAS. HEC-RAS is a hydraulic model software capable of calculating any hydraulic river study including flood. In this study, the Lower Mekong River with approximately 50 km length was selected to delineate flood map from 2000 until 2013 and also 10-year return period map. The available data are 11 years of the measured water level at the upstream and downstream stations, 18 surveyed cross-sections and DEM with grid cell size 30 m x 30 m were used to understand the recurrence of the floods in the study area. The output from the model was delineated into map including flood extent and flood depth from 2000 until 2013 (without 2009, 2010 and 2012). The results show that flooding varied from year to year; however, the greatest flood was during 2000 and again in 2011. The simulated flood maps were compared with observed data to figure out that the model was accurate for flood mapping. These results will be useful for river engineers, experts, and decision makers to manage river floods.
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