Tonle Sap Lake (TSL) in Cambodia is the largest freshwater body in South‐East Asia and one of the most productive ecosystems in the world. The lake and its ecosystems are widely under threat, however, due to anthropogenic activities occurring inside and outside its basin (e.g., water infrastructure development; land use change), being poorly understood in most aspects. This study provides an updated review of the state of knowledge of the TSL ecosystem, as well as important research directions for sustainable lake environmental management of Tonle Sap Lake by focusing on four major topics, including climate change and hydrology, sediment dynamics, nutrient dynamics and primary and secondary production. The findings of this study suggest anthropogenic activities in the TSL basin, as well as the Mekong, in combination together with climate changes, are key contributing factors in the degradation of the TSL ecosystem. Insufficient accurate data, however, precludes quantitative assessment of such impacts, making it difficult to quantitatively assess and accurately understand the ecosystem process in the lake ecosystem. More efforts are recommended in regard to environmental monitoring in all sub‐basins around TSL, assessing seasonal changes in nutrient and sediment inputs corresponding to water level and flow changes, assessing cumulative impacts of water infrastructure and climate change on the ecosystem dynamics, and elucidation of ecosystem processes within the lake's internal system.

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Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000-2003) and validation (2004-2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of the annual maximum inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin.