Spatio-temporal patterns of soil erosion and suspended sediment dynamics in the Mekong River Basin

Abstract

Understanding of the distribution patterns of sediment erosion, concentration and transport in river basins is critically important as sediment plays a major role in river basin hydrophysical and ecological processes. In this study, we proposed an integrated framework for the assessment of sediment dynamics, including soil erosion (SE), suspended sediment load (SSL) and suspended sediment concentration (SSC), and applied this framework to the Mekong River Basin. The Revised Universal Soil Loss Equation (RUSLE) model was adopted with a geographic information system to assess SE and was coupled with a sediment accumulation and a routing scheme to simulate SSL. This framework also analyzed Landsat imagery captured between 1987 and 2000 together with ground observations to interpolate spatio-temporal patterns of SSC. The simulated SSL results from 1987 to 2000 showed the relative root mean square error of 41% and coefficient of determination (R(2)) of 0.89. The polynomial relationship of the near infrared exoatmospheric reflectance and the band 4 wavelength (760-900nm) to the observed SSC at 9 sites demonstrated the good agreement (overall relative RMSE=5.2%, R(2)=0.87). The result found that the severe SE occurs in the upper (China and Lao PDR) and lower (western part of Vietnam) regions. The SSC in the rainy season (June-November) showed increasing and decreasing trends longitudinally in the upper (China and Lao PDR) and lower regions (Cambodia), respectively, while the longitudinal profile of SSL showed a fluctuating trend along the river in the early rainy season. Overall, the results described the unique spatio-temporal patterns of SE, SSL and SSC in the Mekong River Basin. Thus, the proposed integrated framework is useful for elucidating complex process of sediment generation and transport in the land and river systems of large river basins.

Historical and Operational Monitoring of Surface Sediments in the Lower Mekong Basin Using Landsat and Google Earth Engine Cloud Computing

“…However, the relative error (RE) for the entire validation dataset of the model is approximately 43%, with a RE of 44% and 41% for dry season and wet season samples respectively. While this RE is above the desired value RE of 35% set by NASA's Ocean Biology and Biogeochemistry Program [65], we are confident in our model because this relative error corresponds to a root-mean-square error ( Previous studies of suspended sediment in the Lower Mekong Basin (using both in situ and remote sensing approaches) have focused largely on the mainstem water quality stations [8,27,34]. In this study we used all available data (mainstem and tributaries) to develop a empirical model of SSSC that can be applied throughout the entire basin.…”

“…Previous efforts to estimate SSSC and TSS from remote sensing data sources in the Lower Mekong Basin have focused on the main stem of the Mekong River or on the Mekong Delta, but not both. For example, Suif et al [34] developed an empirical model using the near-infrared band on Landsat TM as well as the blue, green, and red bands in a multiple linear regression along the Mekong River. In another recent study, Duc et al [35] found a strong correlation between the 1st principle component of Landsat TM and ETM+ imagery and SSSC in the Mekong Delta.…”

Suspended Sediment Transport Responses to Increasing Human Activities in a High-Altitude River: A Case Study in a Typical Sub-Catchment of the Yarlung Tsangpo River

“…It is estimated that approximately one-third of the global sediment load to oceans is generated from the large rivers originating from the Tibetan Plateau and its neighboring regions [2]. In recent decades, the sediment regimes of these rivers have attracted increasing attention due to public concerns about climate change and increasing human activities [3][4][5][6][7][8][9][10][11]. Human activities, such as infrastructure development, soil conservation, and sand excavation, have played an important role in sediment load variations [12][13][14][15].…”

A Sediment Detection Analysis with Multi Sensor Satellites: Caspian Sea and Persian Gulf case studies

“…Coastal sea and ocean waters are often characterized by a high value of variability in the concentration of Suspended Particulate Matter (SPM) [5]. For detecting suspended sediments events in coastal sea and ocean waters, researchers can use low or high-spatial resolution satellites data such as those provide by: SeaWiFs [4], MERIS [6], GOCI image [7], Landsat [8], Sentinel-3 [9], MODIS [10]-Aqua and Terra [11]. In particular, multispectral sensors like MODIS offer a high number of spectral bands to detect, identify, classify, describe and guarantee daily image frequency [12].…”