•Sediment is of great importance for the Lancang-Mekong River (LMR) ecosystem, providing essential ecological services for local communities.•A new index was proposed to quantify the regional sediment composition by considering riverbed deposition.•The contribution ratio of the upstream area could be overestimated without considering sediment deposition onto the riverbed.

Reliable assessment of the natural interactions in large river-lake systems is vital for water supply planning, flood regulation, and ecosystem services. The existing interaction between the Mekong River (MR), Tonle Sap River (TSR), and Tonle Sap Lake (TSL) has been further complicated due to the reverse flow phenomenon (RF) beyond ongoing anthropogenic activities and climate change. While continuous observations, insufficient data, and event-driven measurement have remained a challenge for such a large basin, accurate prediction of the RF period and water level influenced would pave the way in providing effective remedies for side effects in this area for future scenarios. In this study, the RF periods were investigated employing the K-Nearest Neighbors (KNN) and Ensemble Bagged Tree (EBT) classification algorithms that showed comparable results with observed periods. The predicted periods were then used to predict the daily water level at the confluence where the MR and TSR join using Long Short-Term Memory (LSTM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. Although both models yielded highly accurate water level predictions, a slightly better performance was attributed to the LSTM model. Regarding the TSR's role in the RF period, results highlighted its significant role in extending the RF period in some years, despite its much lower streamflow than the MR. The developed model could be used as a reliable machine learning (ML) framework where there is a need for highly accurate data regarding RF and water levels for TSL and its environs.

The Lancang-Mekong River (LMR) basin has historically been affected by severe floods and is likely to suffer further flood events with higher peaks and longer duration in the future due to climate change, which calls for collective action to respond. This study examined the potential flood control effect of coordinated operation of the LMR transboundary multi-reservoir system by establishing a distributed hydrological model coupled with a reservoir operating model, simulating the runoff of 13 tributaries and 5 mainstream sections. Results show that:① Flood magnitude at the five sections along the Mekong River is significantly reduced by mitigating flood recurrence from 200 years to 20-50 years.② In terms of flood control, the left-bank tributaries contribute more than the right bank. Tributaries with relatively higher flood control capacity are:Lancang, Nam Ou, Nam Ngum and Nam Theun, Nam Mun, Se Kong and Se San. ③ Different tributaries play a major role in flood control across regions. Luang Prabang's main flood control tributaries are Lancang and Nam Ou. At Nakhon Phanom, Lancang's flood control contribution is same as Nam Ngum and Nam Theun's sum. In the downstream of Pakse, the flood control contribution of Nam Mun and Se Kong are higher than Lancang. This research provides a reference for transboundary flood control cooperation between riparian countries, which face an important opportunity underpinned by the Lancang-Mekong Cooperation Mechanism (LMC).

Mekong River Basin (MRB) has suffered huge injuries and property losses from frequent floods, which would very likely witness an increase of flood magnitude and frequency under climate change. In this chapter, we set up a distributed hydrological model (THREW) to provide fundamental analysis of the flood characteristics in the MRB, the simulation period is 1991–2016, the spatial coverage is the whole basin except the delta region due to lack of reliable topographic data. Two main types of flood in the MRB which are riverine flood and flash flood are discussed. Flood peak frequency at mainstream stations along Mekong are achieved by Pearson-III Frequency Curve Fitting. The annual flood volume and duration at mainstream stations along Mekong River are calculated and analyzed. Taking the flood volume of damaging floods at Stung Treng station (at lower reach of Mekong mainstream) as subject, the THREW model is used to analyze flood’s travel time and regional composition, which would benefit flood prevention and water resources management from a whole-basin view.

The Lancang-Mekong River is one of the largest transboundary river shared by China and Southeast Asian countries. In recent years, the sediment issue in this large river has received extensive attention due to nutrient-rich sediment decreasing which is critical to ecological health, aquatic habitats, shipping, coastal erosion and agricultural and fisheries production. A wide range of studies on sediment yield, transportation and trapped by reservoirs are summarized. The main controversial topics include where the most sediment comes from, how to explain the abnormal relationship among suspended sediment loads observed along the mainstream, and the quantification of effect of human activities on sediment load change. There are five main issues unsolved, including unreliable observed sediment data in Lower Mekong River, weak basis of total sediment load of 160×106 t/a, low spatiotemporal resolution in model simulation, insufficient study of the land use change impacts in recent decades and inadequate research on sediment except for suspended sediment. Several possible ways to overcome these difficulties are also discussed.

This study investigates the dependency of the evaluation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) rainfall product on the gauge density of a ground-based rain gauge network as well as rainfall intensity over five subregions in mainland China. High-density rain gauges (1.5 gauges per 100 km(2)) provide exceptional resources for ground validation of satellite rainfall estimates over this region. Eight different gauge networks were derived with contrasting gauge densities ranging from 0.04 to 4 gauges per 100 km(2). The evaluation focuses on two warm seasons (April-October) during 2014 and 2015. The results show a strong dependency of the evaluation metrics for the IMERG rainfall product on gauge density and rainfall intensity. A dense rain gauge network tends to provide better evaluation metrics, which implies that previous evaluations of the IMERG rainfall product based on a relatively low-density gauge network might have underestimated its performance. The decreasing trends of probability of detection with gauge density indicate a limited ability to capture light rainfall events in the IMERG rainfall product. However, IMERG tends to overestimate (underestimate) light (heavy) rainfall events, which is a consistent feature that does not show strong dependency on gauge densities. The results provide valuable insights for the improvement of a rainfall retrieval algorithm adopted in the IMERG rainfall product.