Storage and release of surface water by reservoirs can alter the natural streamflow pattern of rivers with negative impacts on the environment. Such reservoir-driven river regulation is poorly understood at a global scale due to a lack of publicly available in-situ data on reservoir operations. However, with rapid advancements in satellite remote sensing-based tracking of reservoir state, this gap in data availability can be bridged. In this study, we modeled regulated flow of rivers using only satellite-observed reservoir state and hydrological modeling forced also with satellite precipitation data. We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. ResORR successfully captured the peak of the regulated river flow altered by hydropower dam and flood control operations during the devastating floods of 2018 in the South Indian state of Kerala. On average, ResORR improved regulation river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

Satellite radar altimeters have been used to monitor sea level changes and ice sheet elevation changes for more than 3 decades. Over mountain glaciers, radar altimetry has limited applications due to contaminated radar waveforms caused by complex glacier surfaces and steep terrains. In this study, we develop a glacier-threshold method (GTM) to determine glacier elevation changes over mountain glaciers in Alaska. The GTM can detect and remove invalid elevation observations from the TOPEX/Poseidon (T/P) and Jason-2 (J2) altimeters, creating usable elevation observations from 16–92% of the raw observations. The selected elevations are used to construct long-term time series of Alaskan glacier elevation changes over 1993–2002 (T/P) and 2008–2016 (J2) at 47 sites. A crossover analysis and a Lidar comparison confirm the result from T/P and J2. Our finding shows that most of the Alaskan glaciers studied have continued to decline in recent years. The largest declining rate is -11.06 ± 0.35 m/yr over Klutlan Glacier, followed by Chitina Glacier at -8.82 ± 0.12 m/yr. Glacier thickening occurred in some accumulation zones, such as Hubbard Glacier and Logan Glacier, and also at some glacier terminuses. The mechanisms of these elevation changes are discussed using climate datasets. It is suggested that changes in environmental factors such as precipitation, air temperature and sea water temperature influence the shifts in the trends of glacier elevation changes. A sophisticated processing system and altimeter data from repeat missions can facilitate long-term monitoring of small-scaled glaciers for a better understanding of glacier dynamics.