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InfeRes is a Python package designed for the automated extraction of reservoir dynamics—including time series of water surface area, water level, and storage volume—by leveraging satellite imagery from Google Earth Engine (Landsat series, Sentinel-2) and high-resolution digital elevation models (30 m DEMs). 

Abstract Decarbonizing the electricity sector requires massive investments in generation and transmission infrastructures that may impact both water and land resources. Characterizing these effects is key to ensure a sustainable energy transition. Here, we identify and quantify the unintended consequences of decarbonizing the China Southern Power Grid, China’s second-largest grid. We show that reaching carbon neutrality by 2060 is feasible; yet, doing so requires converting 40,000 square kilometers of land to support solar and wind as well as tapping on rivers to build ~32 gigawatts of hydropower. The impact of wind and solar development would span across multiple sectors, since crop and grassland constitute 90% of the identified sites. The construction of new dams may carry major externalities and trickle down to nearby countries, as most dams are located in transboundary rivers. Curbing the international footprint of this decarbonization effort would require additional investments (~12 billion United States dollars) in carbon capture technologies. 

Abstract Characterizing the impact of human actions on terrestrial water fluxes and storages at multi‐basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human‐water interactions in large‐scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper‐resolution” hydrological models (0.1–1 km), multi‐sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human‐water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper‐resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort. 

Abstract Despite having offered important hydroclimatic insights, streamflow reconstructions still see limited use in water resources operations, because annual reconstructions are not suitable for decisions at finer time scales. The few attempts toward sub‐annual reconstructions have relied on statistical disaggregation, which uses none or little proxy information. Here, we develop a novel framework that optimizes proxy combinations to simultaneously produce seasonal and annual reconstructions. Importantly, the framework ensures that total seasonal flow matches annual flow closely. This mass balance criterion is necessary to avoid misguiding water management decisions, such as the allocation of water rights or dam release decisions. Using the framework, and leveraging a multi‐species network of ring width and celluloseO in Southeast Asia, we reconstruct seasonal and annual inflow to Thailand's largest reservoir. The reconstructions are statistically skillful. Furthermore, they preserve the mass balance well: the differences are mostly within 10% of the mean annual flow. As a result, the reconstructions provide more reliable estimates of the seasonal and annual surface water availability. This work is one step closer toward operational usability of streamflow reconstruction in water resources management.