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Our understanding of the full range of natural variability in streamflow, including how modern flow compares to the past, is poorly understood for the Upper Indus Basin because of short instrumental gauge records. To help address this challenge, we use Hierarchical Bayesian Regression with partial pooling to develop six centuries long (1394–2008 CE) streamflow reconstructions at three Upper Indus Basin gauges (Doyian, Gilgit, and Kachora), concurrently demonstrating that Hierarchical Bayesian Regression can be used to reconstruct short records with interspersed missing data. At one gauge (Partab Bridge), with a longer instrumental record (47 years), we develop reconstructions using both Bayesian regression and the more conventionally used principal components regression. The reconstructions produced by principal components regression and Bayesian regression at Partab Bridge are nearly identical and yield comparable reconstruction skill statistics, highlighting that the resulting tree ring reconstruction of streamflow is not dependent on the choice of statistical method. Reconstructions at all four reconstructions indicate that flow levels in the 1990s were higher than mean flow for the past six centuries. While streamflow appears most sensitive to accumulated winter (January–March) precipitation and summer (May–September) temperature, with warm summers contributing to high flow through increased melt of snow and glaciers, shifts in winter precipitation and summer temperatures cannot explain the anomalously high flow during the 1990s. Regardless, the sensitivity of streamflow to summer temperatures suggests that projected warming may increase streamflow in coming decades, though long‐term water risk will additionally depend on changes in snowfall and glacial mass balance. Key Points: Tree ring reconstructions of streamflow in the Upper Indus Basin show wetter conditions in the 1990s compared to the last 600 yearsReconstructions are insensitive to the choice of statistical method used (principal components versus Bayesian regression)Streamflow is most sensitive to winter precipitation and summer temperature, but anomalies in these seasons cannot explain recent high flow [ABSTRACT FROM AUTHOR]

Storage and controlled distribution of water have been key elements of a human strategy to overcome the space and time variability of water, which have been marked by catastrophic droughts and floods throughout the course of civilization. In the United States, the peak of dam building occurred in the mid-20th century with knowledge limited to the scientific understanding and hydrologic records of the time. Ecological impacts were considered differently than current legislative and regulatory controls would potentially dictate. Additionally, future costs such as maintenance or removal beyond the economic design life were not fully considered. The converging risks associated with aging water storage infrastructure and uncertainty in climate in addition to the continuing need for water storage, flood protection, and hydropower result in a pressing need to address the state of dam infrastructure across the nation. Decisions regarding the future of dams in the United States may, in turn, influence regional water futures through groundwater outcomes, economic productivity, migration, and urban growth. We advocate for a comprehensive national water assessment and a formal analysis of the role dams play in our water future. We emphasize the urgent need for environmentally and economically sound strategies to integrate surface and groundwater storage infrastructure in local, regional, and national water planning considerations. A research agenda is proposed to assess dam failure impacts and the design, operation, and need for dams considering both paleo and future climate, utilization of groundwater resources, and the changing societal values toward the environment. [ABSTRACT FROM AUTHOR]

Sea surface temperatures in the equatorial Pacific exhibited persistent warming beginning in January 2015 and by November 2015 were predicted to deliver a strong El Niño in the coming winter, perhaps the strongest on record, with wide-reaching hydroclimate impacts. As ENSO continues to evolve over the 2016 winter season, we ask the question: To what degree could ENSO-related hydroclimate impacts have been anticipated a season prior? We provide a retrospective hydroclimate outlook for the 2016 winter, accounting for the prevailing uncertainties that often pose a challenge to local resource managers seeking to make use of seasonal forecasts. [ABSTRACT FROM AUTHOR]

Especially for extreme precipitation or floods, there is considerable spatial and temporal variability in long term trends or in the response of station time series to large-scale climate indices. Consequently, identifying trends or sensitivity of these extremes to climate parameters can be marked by high uncertainty. When one develops a nonstationary frequency analysis model, a key step is the identification of potential trends or effects of climate indices on the station series. An automatic clustering procedure that effectively pools stations where there are similar responses is desirable to reduce the estimation variance, thus improving the identification of trends or responses, and accounting for spatial dependence. This paper presents a new hierarchical Bayesian approach for exploring homogeneity of response in large area data sets, through a multicomponent mixture model. The approach allows the reduction of uncertainties through both full pooling and partial pooling of stations across automatically chosen subsets of the data. We apply the model to study the trends in annual maximum daily stream flow at 68 gauges over Germany. The effects of changing the number of clusters and the parameters used for clustering are demonstrated. The results show that there are large, mainly upward trends in the gauges of the River Rhine Basin in Western Germany and along the main stream of the Danube River in the south, while there are also some small upward trends at gauges in Central and Northern Germany. [ABSTRACT FROM AUTHOR]