The value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large-scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled-water energy model that is subject to reservoir inflow forecasts with different levels of accuracy. We implement the modelling framework on a real-world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro-dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

The Monsoon Asia region is home to ten of the world’s biggest rivers, supporting the lives of 1.7 billion people who rely on streamflow for water, energy, and food. Yet, a synthesized understanding of multi-centennial streamflow variability for this region is lacking. To fill this gap, we produce the first large scale streamflow reconstruction over Monsoon Asia (62 stations in 16 countries, 813 years of mean annual flow). In making this reconstruction, we develop a novel, automated, climate-informed, and dynamic reconstruction framework that is skillful over most of the region. We show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans. We also show how these oceanic teleconnections change over space and time. By characterizing past and present hydroclimatic variability, we provide a platform for assessing the impact of future climatic changes and informing water management decisions.

Selective water release from the deeper pools of reservoirs for energy generation alters the temperature of downstream rivers. Thermal destabilization of downstream rivers can be detrimental to riverine ecosystem by potentially disturbing the growth stages of various aquatic species. To predict this impact of planned hydropower dams worldwide, we developed, tested and implemented a framework called ‘FUture Temperatures Using River hISTory’ (FUTURIST). The framework used historical records of in-situ river temperatures from 107 dams in the U.S. to train an artificial neural network (ANN) model to predict temperature change between upstream and downstream rivers. The model was then independently validated over multiple existing hydropower dams in Southeast Asia. Application of the model over 216 planned dam sites afforded the prediction of their likely thermal impacts. Results predicted a consistent shift toward lower temperatures during summers and higher temperatures during winters. During Jun-Aug, 80% of the selected planned sites are likely to cool downstream rivers out of which 15% are expected to reduce temperatures by more than 6˚C. Reservoirs that experience strong thermal stratification tend to cool severely during warm seasons. Over the months of Dec-Feb, a relatively consistent pattern of moderate warming was observed with a likely temperature change varying between 1.0 to 4.5˚C. Such impacts, homogenized over time, raise concerns for the ecological biodiversity and native species. The presented outlook to future thermal pollution will help design sustainable hydropower expansion plans so that the upcoming dams do not face and cause the same problems identified with the existing ones.

The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world, but with contrasting conclusions on expected hydropower production and economic gains. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958-2000 with three operating schemes: basic control rules, perfect forecast-informed, and realistic forecast-informed. The realistic forecasts are issued by bespoke models, based on lagged global and local hydroclimatic variables, predicting seasonal monthly dam inflows. Results show that most dams (94%) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time to fill and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25% of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design characteristics but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and have the potential to reap benefits from forecast-informed operations, and dams with strong potential to benefit from forecast-informed operations but lack forecast accuracy. Overall, these results represent a first qualitative step towards informing site-specific hydropower studies.

Water system operations require subannual streamflow data—e.g., monthly or weekly—that are not readily achievable with conventional streamflow reconstructions from annual tree rings. This mismatch is particularly relevant to highly seasonal rivers such as Thailand’s Chao Phraya. Here, we combine tree ring width and oxygen isotope (δ18O) from Southeast Asia to produce 254-year, monthly-resolved reconstructions for all four major tributaries of the Chao Phraya. From the reconstructions, we derive subannual streamflow indices to examine past hydrological droughts and pluvials, and find coherence and heterogeneity in their histories. The monthly resolution reveals the spatiotemporal variability in wet season timing, caused by interactions between early summer typhoons, monsoon rains, catchment location, and topography. Monthly-resolved reconstructions, like the ones presented here, not only broaden our understanding of past hydroclimatic variability, but also provide data that are functional to water management and climate-risk analyses, a significant improvement over annual reconstructions.

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. Attempts towards 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 water allocation. Using the framework, and leveraging a multi-species network of ring width and cellulose δ18O in Southeast Asia, we reconstruct seasonal and annual inflow to Thailand’s largest reservoir. The reconstructions are statistically skillful. This work is one step closer towards operational usability of streamflow reconstruction in water resources management.

The Greater Mekong Subregion is a transnational area bound together by the Mekong River basin and its immense hydropower resources, historically seen as the backbone of regional economic development. The basin is now punctuated by several dams, successful in attracting both international investors and fierce criticisms for their environmental and societal impacts. Surprisingly, no attention has been paid so far to the actual performance of these infrastructures: is hydropower supply robust with respect to the hydro-climatic variability characterizing Southeast Asia? When water availability is altered, what are the implications for power production costs and CO2 emissions? To answer these questions, we focus on the Laotian–Thai grid—the first international power trade infrastructure developed in the region—and use a power system model driven by a spatially-distributed hydrological-water management model. Simulation results over a 30-year period show that production costs and carbon footprint are significantly affected by droughts, which reduce hydropower availability and increase reliance on thermoelectric resources. Regional droughts across the Mekong basin are of particular concern, as they reduce the export of cheap hydropower from Laos to Thailand. To put the analysis into a broader climate-water-energy context, we show that the El Niño Southern Oscillation modulates not only the summer monsoon, but also the power system behaviour, shaping the relationship between hydro-climatological conditions, power production costs, and CO2 emissions. Overall, our results and models provide a knowledge basis for informing robust management strategies at the water-energy scale and designing more sustainable power plans in the Greater Mekong Subregion.

The hydropower fleet built in the Upper Mekong River, or Lancang, currently consists of eleven mainstream dams that can control about 55% of the annual flow to Northern Thailand and Laos. The operations of this fleet have become a source of controversy between China and downstream countries, with these dams often considered the culprit for droughts and other externalities. Assessing their actual impact is a challenging task because of the chronic lack of data on reservoir storage and operations. To overcome this challenge, we focus on the ten 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 data (Jason) 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 only two years, and that their operations did not change in response to the drought that occurred in the region in 2019-2020. Deciphering these operating strategies could help enrich existing monitoring tools and hydrological models, thereby supporting riparian countries in the design of more cooperative water-energy policies.

Increased flood risks have been projected, but with large uncertainties, in the Kabul River Basin (Afghanistan and Pakistan). To place future changes in a long-term perspective, we produce a 382-year precipitation reconstruction for the basin using seven tree-ring chronologies of old-growth conifers from the Hindu Kush Mountains, a monsoon-shadow area. The reconstruction proves robust over rigorous cross-validations (R2 = 0.60, RE = 0.60, CE = 0.53). The full reconstruction (1637–2018) reveals a steady decline in the low end of the precipitation distribution, implying increasing drought risks. We show that droughts are getting more severe, shorter, and more frequent, interspersed with more frequent pluvials in the past century. Drought risks, compounded with projected flood intensification, pose significant threats for this transboundary river. Therefore, future water management needs to account for both flood and drought risks and be informed by long-term hydroclimatic variability.