Over the past half century since 1975, the development and improvement of meteorological geostationary (GEO) satellites have played a pivotal role in observing cloud dynamics and subsequently in advancing spaceborne precipitation estimation. Infrared (IR) observation from GEO satellites offers unique advantages, such as broad spatial coverage, high temporal resolution, and long-term consistency, motivating extensive research to unlock the potential of GEO IR-based data for capturing precipitation structure and dynamics. This article reviews the major developments and milestone achievements of GEO IR-based precipitation estimation over the past five decades and summarizes the future prospects, including potential directions and remaining challenges. By examining the history of global GEO satellites and more than 100 references in this domain, we categorize the development of GEO IR-based precipitation estimation methodology and technology into three distinct stages: 1) the Exploration Phase (1975 to ca. 1995), 2) the Growth Phase (ca. 1995 to ca. 2015), and 3) the Exploitation Phase (ca. 2015 to the present). As we transition into an emerging new stage, these efforts collectively point toward multispectral retrievals, lifecycle-aware machine learning (ML), smart sensing, and advanced multisource integration as key directions shaping the future of GEO IR-based precipitation estimation. In summary, GEO IR-based precipitation estimation has made substantial contributions over the past half century and will play an increasingly important role with great potential in future precipitation science.

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster response. Traditional tools such as rain gauges and radar networks, though effective, are limited by sparse coverage in remote areas and radar constraints such as beam blockage and increasing beam height with range, which reduce near-surface accuracy. Satellite observations address these challenges by providing global coverage with fine spatial and temporal resolution. Many precipitation products combine geosynchronous thermal infrared (IR) and passive microwave (PMW) data. PMW sensors offer detailed atmospheric profiles but are restricted to infrequent overpasses and increasing reliance on smaller satellites with higher-frequency channels, which are less sensitive to liquid precipitation. In contrast, IR sensors provide consistent, high-frequency global observations, making them valuable for near-real-time estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-U-Net (PU-Net or PERSIANN V3), a quasi-global algorithm covering 60 degrees N-60 degrees S that combines IR data, monthly climatology, and the U-Net architecture to produce half-hourly precipitation estimates at 0.04 degrees resolution. The product is evaluated against Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and PERSIANN Dynamic Infrared-Rain Rate (PDIR-Now) for 2022-23. Results show that PU-Net closely matches its training target, IMERG V07 Final, at the global scale, and its performance is further evaluated against Stage IV as a reference over contiguous United States (CONUS). Training PU-Net on IMERG (2016-21) leverages a high-quality, integrated PMW-IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PU-Net avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Abstract Data‐driven rainfall–runoff models have advanced rapidly, yet the majority of large‐scale applications still rely on lumped inputs that smooth out spatial variability in precipitation, temperature, and landscape properties. This simplification can introduce substantial biases in flood peaks, hydrograph timing, and water balance estimates. Here, we develop a deep learning framework that ingests spatially distributed (gridded) meteorological forcings and is evaluated across 921 catchments in North America using the CAMELS‐SPAT data set. We compare two strategies for fusing spatial and temporal information, namely a spatial‐to‐temporal (S2T) architecture and a temporal‐to‐spatial (T2S) architecture, and assess the added value of gridded catchment attributes and finer spatial resolutions. The T2S approach consistently achieves the best performance, increasing median Nash–Sutcliffe Efficiency (NSE) from 0.60 (lumped baseline) to 0.65 and Kling–Gupta Efficiency (KGE) from 0.63 to 0.69, with NSE improvements observed in 75.4% of catchments. Performance gains are consistent across catchment sizes and aridity regimes, with enhanced skill in low‐flow, high‐flow, and peak timing representations. Integrated Gradients (IG) analysis shows that the model learns physically coherent spatial behavior, highlighting grid cells near the outlet and identifying dominant meteorological drivers of peak runoff events. These results demonstrate the practical value of spatially distributed forcings for data‐driven hydrologic modeling and outline a pathway toward interpretable, large‐scale streamflow prediction.

Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting.

Accurate precipitation estimation is essential for water resource management and disaster mitigation, particularly in regions characterized by complex terrain and climate variability. This study proposes a U-Net-based multi-task deep learning framework that simultaneously performs precipitation zone classification and intensity reconstruction to correct biases and merge precipitation data in the Lancang-Mekong River Basin. The model includes a classification branch for generating precipitation masks and a reconstruction branch for refining intensity estimates. Two satellite-based precipitation products (PERSIANN-CDR and TRMM 3B42 V7), along with the ERA5 reanalysis dataset, are bias-corrected and merged using ground gauge data (2001-2019). The results demonstrate that this framework notably improves accuracy and consistency across varying timescales. The merged product shows substantially improved performance, reducing the root mean square error (RMSE) to 2.61 mm/day and elevating the correlation with ground observations to 0.94, outperforming individual original datasets (RMSE up to 8.45 mm/day and correlation as low as 0.46). At the monthly timescale, the merged dataset more effectively captures regional precipitation variability, achieving correlation coefficients exceeding 0.8. It accurately captures seasonal patterns and estimates light to moderate rainfall, although challenges remain for extreme rainfall events. Spatially, upstream terrain-induced biases are reduced, while downstream, the detection of moderate and heavy rainfall is improved. These findings underscore the novelty and effectiveness of the developed framework in integrating heterogeneous satellite data through deep learning methodologies. The resulting high-resolution dataset (0.25 degrees/daily, 2001-2019) offers improved accuracy and spatial coherence, thereby offering substantial support for hydrological analyses and climate research in environmentally diverse regions.

Precipitation is a critical component of the hydrologic cycle, affecting water availability and flood risk. Its estimation can help protect lives and mitigate property damage. Machine learning (ML) models have become popular for estimating precipitation; however, when choosing a target dataset, certain characteristics must be considered. In situ data are ideal to train an ML model, but alternatives must be explored when such data are sparse. Thus, a U-Net was trained three times using the Climate Prediction Center (CPC)4-km infrared data as input and different precipitation target dataIntegrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERG Final), and CPC Combined Passive Microwave Precipitation (MWCOMB). The results were evaluated against Stage IV. The MRMS model performed best with a correlation coefficient (CC) of 0.50 and a probability of detection (POD) of 0.90 at an hourly scale. The IMERG Final model followed with a CC of 0.47 and a POD of 0.85, making it a viable alternative given its temporal resolution and data availability. The MWCOMB model overestimated precipitation and had a CC and POD of 0.45 and 0.82. Across daily and monthly scales, MRMS consistently outperformed the other models, with IMERG Final ranking second. Due to MWCOMB's persistent overestimation, different data preparation may be required for effective use. In conclusion, MRMS is the most suitable dataset for ML-based precipitation estimation due to its in situ nature and robust performance; however, IMERG Final is a viable alternative.

Pre-trained models like FourCastNet, Pangu and GraphCast have gained popularity in the meteorological field. In hydrology, data-driven rainfall-runoff models based on long short-term memory (LSTM) networks have been successfully applied for various purposes. As large-sample hydrological datasets (e.g., Caravan) continue to grow, it is foreseeable that pre-trained models tailored for hydrology will emerge. These pre-trained models have the potential to bypass the need for training data-driven models from scratch, enabling us to focus more swiftly on customized applications. Additionally, they offer opportunities explore model performance in changing environment, which is also a key consideration when using data-driven models in unseen scenarios. However, the hydrological field has seen limited attempts to employ, transfer, and fine-tune pre-trained models. This study aims to explore the possibility of using fine-tuning techniques to achieve a smooth transition of LSTM-based rainfall-runoff models from pre-training to post-application scenarios. By utilizing ERA5-Land reanalysis precipitation data within the Caravan dataset, we calibrated a pre-trained LSTM model for runoff simulation. Subsequently, we transitioned the model to use near-real-time satellite precipitation estimates as the input, targeting satellite-driven predictions. Our results show that fine-tuning parameters lead to improvements in various metrics, including the Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and hydrological signature metrics such as high and low flows, compared to outcomes without parameter fine-tuning. Specifically, fine-tuning using locally calibrated models enhanced performance in 73.5% of the basins. In contrast, the results of fine-tuning regional models were mixed; while it benefited 55.1% of the basins, it also led to a deterioration in model performance in 44.9% of cases. This study is a pioneering exploration of the adaptability of LSTM models from pre-training to post-application. It also lays the groundwork for future investigations aimed at enhancing the adaptability of data-driven models to the impacts of changing environment.

Addressing uncertainties in hydrological modelling is critical for improving model accuracy. Pre-processing and post-processing techniques offer effective ways to reduce these uncertainties, but strategic selection and combination of these methods with machine learning models require further exploration. In this study, we introduced input uncertainty into a hydrological model using satellite precipitation data as a case study. We investigated the effects of pre-processing using Random Forest (RF), post-processing using RF and Long Short-Term Memory (LSTM) networks, and their combination within a comprehensive modelling framework for daily streamflow simulation across 522 sub-basins. We used the Kling-Gupta Efficiency (KGE) and its three components-correlation, bias, and variability to assess model performance. Our analysis revealed that pre-processing with RF alone substantially improved model performance over raw simulation, especially in terms of KGE and correlation. Pre-processing improved KGE in 96.2% of sub-basins (average increase: 0.38) and enhanced correlation in 100% of sub-basins (average increase: 0.164). Post-processing with RF alone also improved performance but was less effective, enhancing KGE in 83.0% of sub-basins (average increase: 0.24). The combined approach delivered the most significant gains, improving KGE in 92.7% of sub-basins (average increase: 0.42) and achieving 100% improvement in correlation (average increase: 0.161), while also resulting in better bias and variability scores. Comparisons indicated that pre-processing is more effective than post-processing. Further analysis showed that different post-processing models, RF and LSTM, yielded similar results with marginal differences. This study highlights the importance of carefully selecting and combining pre-processing and postprocessing strategies to optimize hydrological model performance.

The 2022-23 winter in the western United States, particularly in Southern California, experienced unusually wet and cold conditions, prompting vigilant water management. This study chronicles the water year, highlighting the challenges state water managers faced as California shifted from extreme drought to elevated flood risks due to an unprecedented "weather whiplash" and a subsequent record-setting snowpack. By analyzing precipitation and temperature data from 2002 to 2023, the research highlights the anomalous nature of these variables in California during this period. It focuses on the impacts of atmospheric rivers (ARs) due to their proven influence on seasonal precipitation patterns and intensities, examining their hydrologic impacts-specifically, snow water equivalent (SWE) in Sierra Nevada and reservoir storage-compared to other high precipitation years in California to gauge the effects of this atypical weather on water resources. The study reveals that Southern California's wintertime precipitation in 2022-23 was the highest in over two decades. Precipitation was closely linked to the occurrence of 11 moderate to strong ARs, which alleviated the state's drought conditions-94% of California was drought free by the end of the water year. Additionally, the mean maximum temperature was below the long-term average during spring and summer, decelerating snowpack melt and mitigating flood potential. Notably, 2022-23 saw the most significant increases in SWE and reservoir storage among the years analyzed. This research delves into the complex interplay between AR-driven precipitation, temperature, and snowpack, providing valuable insights into the precarious dynamics of California's regional hydrology with a real-world example.

Satellite‐based precipitation products (SPPs) have gained popularity among researchers due to their utility in hydrologic studies. Several gridded satellite‐based precipitation products with global coverage, such as the Integrated Multi‐satellitE Retrievals for GPM (IMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family of products, are available worldwide. However, the accuracy of these products may vary due to retrieval algorithms or geographic location. Numerous correction techniques have been implemented, and machine learning techniques, especially Deep Neural Networks, have proven to be the most effective in improving precipitation estimation. This study aims to investigate the performance of the PERSIANN‐Dynamic Infrared Rain Rate near real‐time product (PDIR‐Now) in the Western U.S. and assess the effectiveness of three deep learning models including U‐Net, Efficient‐UNet, and a conditional Generative Adversarial Network (cGAN) in correcting biases present in the product. The developed models are expected to be more accurate than traditional methods, as they include digital elevation information and can resolve complex orographic enhancements in precipitation processes. This incorporation will mitigate the bias associated with SPPs, enabling further potential applications in water resource management. The findings revealed that the corrected results, utilizing the Efficient‐UNet and cGAN models, surpassed the original PDIR‐Now product and U‐Net model across various statistical and categorical metrics at different temporal scales. This bias‐correction scheme will enhance the assessment and understanding of precipitation patterns and can be used to improve the quality of precipitation estimates in other regions.

Malaria, a life-threatening disease, remains a major global health challenge, particularly in Africa. While Anopheles gambiae sensu lato has long been the primary vector in Africa, the recent invasion of Anopheles stephensi-an urban malaria vector native to South Asia, poses a growing threat to malaria control and elimination efforts. Understanding An. stephensi environmental suitability and its spread dynamics is critical for designing effective surveillance and vector control strategies. Although previous studies have mapped potential environmental suitability for An. stephensi, most have focused on temperature or environmental variables, overlooking other critical factors affecting mosquito life cycles. Moreover, little is known about the species' historical spread speed or projected expansion. While An. stephensi is already spreading in the region, this study aims to enhance predictive modeling of suitable habitats and identify areas at ongoing or future risk of invasion. Our approach integrates meteorological, environmental, geophysical, and socioeconomic variables, alongside an expanded occurrence dataset and ecologically constrained pseudo-absence sampling. The model achieved an accuracy of 0.93 in predicting An. stephensi locations during the 2021-2024 test period, outperforming previous studies in the region. We analyzed historical spread patterns, revealing a rapid increase in spread speed from 20 km/year in 2012 to over 120 km/year by 2024. Future spread was projected using environmental suitability, road connectivity, and population density, with the spread model achieving a temporal correlation of 0.66. Projections suggest continued expansion into western Ethiopia, southern Somalia, and southern Kenya, with climate change likely to increase environmental suitability in highland regions. This high-resolution, spatiotemporal framework provides actionable insights for current and future transmission hotspots and supports urgent, targeted interventions to mitigate the spread of An. stephensi under a changing climate.

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.

Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.

Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Land surface depressions play a central role in the transformation of rainfall to ponding, infiltration and runoff, yet digital elevation models (DEMs) used by spatially distributed hydrologic models that resolve land surface processes rarely capture land surface depressions at spatial scales relevant to this transformation. Methods to generate DEMs through processing of remote sensing data, such as optical and light detection and ranging (LiDAR) have favored surfaces without depressions to avoid adverse slopes that are problematic for many hydrologic routing methods. Here we present a new topographic conditioning workflow, Depression-Preserved DEM Processing (D2P) algorithm, which is designed to preserve physically meaningful surface depressions for depression-integrated and efficient hydrologic modeling. D2P includes several features: (1) an adaptive screening interval for delineation of depressions, (2) the ability to filter out anthropogenic land surface features (e.g., bridges), (3) the ability to blend river smoothing (e.g., a general downslope profile) and depression resolving functionality. From a case study in the Goodwin Creek Experimental Watershed, D2P successfully resolved 86% of the ponds at a DEM resolution of 10 m. Topographic conditioning was achieved with minimum impact as D2P reduced the number of modified cells from the original DEM by 51% compared to a conventional algorithm. Furthermore, hydrologic simulation using a D2P processed DEM resulted in a more robust characterization on surface water dynamics based on higher surface water storage as well as an attenuated and delayed peak streamflow.

Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Vesta Afzali Gorooh, vafzaligorooh@ucsd.edu

Abstract. Deep learning (DL) models are popular but computationally expensive, machine learning (ML) models are old-fashioned but more efficient. Their differences in hydrological probabilistic post-processing are not clear at the moment. This study conducts a systematic model comparison between the quantile regression forest (QRF) model and probabilistic long short-term memory (PLSTM) model as hydrological probabilistic post-processors. Specifically, we compare these two models to deal with the biased streamflow simulation driven by three kinds of satellite precipitation products in 522 sub-basins of Yalong River basin of China. Model performance is comprehensively assessed by a series of scoring metrics from the probabilistic and deterministic perspectives, respectively. In general, the QRF model and the PLSTM model are comparable in terms of probabilistic prediction. Their performance is closely related to the flow accumulation area of the sub-basin. For sub-basins with flow accumulation area less than 60,000 km2, the QRF model outperforms the PLSTM model in most of the sub-basins. For sub-basins with flow accumulation area larger than 60,000 km2, the PLSTM model has an undebatable advantage. In terms of deterministic predictions, the PLSTM model should be more preferred than the QRF model, especially when the raw streamflow is poorly simulated and used as an input. But if we put aside the model performance, the QRF model is more efficient in all cases, saving half the time than the PLSTM model. This study can deepen our understanding of ML and DL models in hydrological post-processing and enable more appropriate model selection in practice.

Food insecurity, recurrent famine, and poverty threaten the health of millions of African residents. Con-struction of dams and rural irrigation schemes is key to solving these problems. The sub-Saharan Africa International Center of Excellence for Malaria Research addresses major knowledge gaps and challenges in Plasmodium falciparum and Plasmodium vivax malaria control and elimination in malaria-endemic areas of Kenya and Ethiopia where major investments in water resource development are taking place. This article highlights progress of the International Center of Excellence for Malaria Research in malaria vector ecology and behavior, epidemiology, and pathogenesis since its inception in 2017. Studies conducted in four field sites in Kenya and Ethiopia show that dams and irrigation increased the abundance, stability, and productivity of larval habitats, resulting in increased malaria transmission and a greater dis-ease burden. These field studies, together with hydrological and malaria transmission modeling, enhance the ability to predict the impact of water resource development projects on vector larval ecology and malaria risks, thereby facilitating the development of optimal water and environmental management practices in the context of malaria control efforts. Intersectoral collaborations and community engagement are crucial to develop and implement cost-effective malaria control strategies that meet food security needs while controlling malaria burden in local communities.

Dams and reservoirs are essential infrastructures for water resources development and management. However, the performance and safety of dams depend on the hydrologic regime that is altering in a changing climate. This study assessed the impacts of climate change on water availability, regulation function, hydropower generation and flood hazards for major dams in the Upper Yangtze River Basin (UYRB), under the Shared Socioeconomic Pathways (SSPs) scenarios during the twenty-first century. First, the outputs of 14 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) for the medium (SSP245) and high (SSP585) emission scenarios were used to drive a coupled hydrological, dam and hydroelectric model. Then, changes in metrics covering dam inflow and service performance in the future relative to the historical period were quantified. Finally, climate change-induced flood hazards were investigated by extreme value analysis. Our results show that dam inflow in the basin will exhibit an overall increase in annual mean and more extremes in high and low flows. Meanwhile, hydropower generation will exhibit an increase in magnitude and interannual variability, with a marked rise in spilled water. The results also show the likelihood of experiencing a more extended high flow season, which will consequently impact the flood season with the increase in the frequency and magnitude of flood events, necessitating more prudent and optimal operating rules for the dams on the river system. Moreover, the regulation function of dams is expected to strengthen in the flood season and weaken in the dry season. These findings indicate the increasing importance of dams as a critical water infrastructure system and underline the need for existing operations in adaptation to future climate.