This study explores the operating pattern of artificial reservoirs by examining their impact on streamflow through two parameters, residence time and flow alteration, using a purely satellite‐based technique for the Mekong Basin. Overall residence times of individual reservoirs ranged from 0.09 to 4.04 years, while streamflow was altered between 11 and 130% of its natural variability. The current set of reservoirs appears to have increased the residence time of the entire Mekong basin by about 1 month. However, if subbasin variability is considered, the satellite‐based method depicts a different picture. Residence time increases to 4 months when only regulated flows are considered. If low residence time reservoirs on major rivers are excluded and reservoirs on higher stream‐order rivers considered, residence time increases to 1.3 years. Predictable strong seasonal patterns emerged in residence time, where reservoirs experience higher residence time in the dry season and lower residence time in the wet season and residence time varies inversely with precipitation. High variability in reservoir effects on streamflow between reservoirs could not be explained by any reservoir properties (e.g., size, use, location, etc.), highlighting the variability in the human decisions operating these reservoirs. The take‐home message of this study is that satellite observations, in combination with physical models forced with satellite data, can elucidate the spatiotemporal variability of reservoir behavior in ungauged basins of the developing world. We demonstrate in this study that the requirement for ground data to monitor current or historical behavior of dams is not necessary.

The planned Surface Water and Ocean Topography (SWOT) mission, scheduled to launch in 2021, is designed to significantly improve on current lake and reservoir remote monitoring capacity. These improvements are especially important for the Mekong River Basin (MRB), where rapid dam construction puts downstream food security at risk and upstream dam operations are largely unknown to downstream water managers due to lack of upstream hydrologic information. This study aims to answer: How accurately will SWOT estimate reservoir storage change in the MRB? A physically based simulation tool developed by NASA Jet Propulsion Laboratory to mimic SWOT‐like observations of water bodies was applied to 20 reservoirs in the MRB and for 17 of those reservoirs, synthetic SWOT‐based storage change estimates demonstrated high skill, with errors typically less than 8%. The remaining three reservoirs showed higher errors due to complex neighboring topography or long narrow reservoir shape. In comparison with current satellite observations of six reservoirs in the MRB, the synthetic SWOT observations showed between 4% and 10% lower storage change normalized root‐mean‐square error. Finally, the simulated elevation and surface area was successfully used to estimate the area‐elevation relationships for each reservoir, with a median percent difference of 6.9%. These relationships are a potential avenue for multisensor cooperation toward improved understanding of reservoir dynamics. Such cooperation, where observations from many difference remote sensors are synthesized to provide a more complete view of human interactions on the river system, is vital for the MRB as more dams as human influence over the system increases.

The current situation in the Lancang–Mekong River basin is emblematic of the issues faced by many transboundary basins around the world: riparian countries prioritize national water–energy policies and provide limited information on how major infrastructures are operated. In turn, such infrastructures and their management become a source of controversy. Here, we turn our attention to the Upper Mekong River, or Lancang, where a system of 11 mainstream dams controls about 55 % of the annual flow to Northern Thailand and Laos. Yet, assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and dam release decisions. To overcome this challenge, we focus on the 10 largest reservoirs and leverage satellite observations to infer 13-year time series of monthly storage variations. Specifically, we use area–storage curves (derived from a digital elevation model) and time series of water surface area, which we estimate from Landsat images through a novel algorithm that removes the effects of clouds and other disturbances. We also use satellite radar altimetry water level data (Jason and Sentinel-3) to validate the results obtained from satellite imagery. Our results describe the evolution of the hydropower system and highlight the pivotal role played by Xiaowan and Nuozhadu reservoirs, which make up to ∼ 85 % of the total system's storage in the Lancang River basin. We show that these two reservoirs were filled in about 2 years and that their operations were marginally affected by the drought that occurred in the region in 2019–2020. Deciphering these operating strategies will help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water–energy policies.

Some of the world's largest and flood‐prone river basins experience a seasonal flood regime driven by the monsoon weather system. Highly populated river basins with extensive rain‐fed agricultural productivity such as the Ganges, Indus, Brahmaputra, Irrawaddy, and Mekong are examples of monsoon‐driven river basins. It is therefore appropriate to investigate how precipitation forecasts from numerical models can advance flood forecasting in these basins. In this study, the Weather Research and Forecasting model was used to evaluate downscaling of coarse‐resolution global precipitation forecasts from a numerical weather prediction model. Sensitivity studies were conducted using the TOPSIS analysis to identify the likely best set of microphysics and cumulus parameterization schemes, and spatial resolution from a total set of 15 combinations. This identified best set can pinpoint specific parameterizations needing further development to advance flood forecasting in monsoon‐dominated regimes. It was found that the Betts‐Miller‐Janjic cumulus parameterization scheme with WRF Single‐Moment 5‐class, WRF Single‐Moment 6‐class, and Thompson microphysics schemes exhibited the most skill in the Ganges‐Brahmaputra‐Meghna basins. Finer spatial resolution (3 km) without cumulus parameterization schemes did not yield significant improvements. The short‐listed set of the likely best microphysics‐cumulus parameterization configurations was found to also hold true for the Indus basin. The lesson learned from this study is that a common set of model parameterization and spatial resolution exists for monsoon‐driven seasonal flood regimes at least in South Asian river basins.

Earth‐orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real‐time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet the temporal requirements for access to satellite‐based river data remain uncharacterized for time‐sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low‐latency/near‐real‐time satellite products, with an emphasis on the forthcoming SWOT satellite mission. We apply a kinematic wave model to a global hydrography data set and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively. Our findings suggest that a recently proposed ≤2‐day data latency for a low‐latency SWOT product is potentially useful for real‐time river applications.