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The efficient operation of a multipurpose reservoir requires information on high and low flows. However, analyses of inflows for high flows and for low flows are typically done independently. In this paper, we considered the joint dependence of the low flow on the preceding high flow volume and duration for the wet season in the Three Gorges region of the Yangtze River Basin in China. High flow volume and duration were found to have a strong association with the annual minimum 7‐day flow in Cuntan, Wanxian, and Yichang stations. Furthermore, we identified the Arctic Oscillation, Pacific Decadal Oscillation, and snow cover in the Tibetan Plateau to have statistically significant teleconnections with the annual minimum 7‐day flow. Bayesian models that consider a different level of pooling of the site by site regressions were then developed for the annual minimum 7‐day flow conditional on the climate indices and high flow volume (or duration). The full pooling model performed best, suggesting that a homogeneous regional response is best identified given the global climate predictors. Statistics such as the deviance information criterion and reduction of error, coefficient of efficiency, and coverage rate under cross validation indicate the good performance of the model. Snow cover in the western Tibetan Plateau and high flow volume were identified as the most influential factors of the annual minimum 7‐day flow through their impact on water storage in the basin. Recent simulations since June 2003, when the Three Gorges Dam operation started, were used to analyse the effect of dam operation on the annual minimum 7‐day flow. A comparison of observations and predictions during the post‐dam period demonstrated that the dam operation effectively modifies the annual minimum 7‐day flow period to have higher flows. [ABSTRACT FROM AUTHOR]

We present evidence that the global juxtaposition of major assets relevant to the economy with the space and time expression of extreme floods or droughts leads to a much higher aggregate risk than would be expected by chance. Using a century long, globally gridded time series that indexes net water availability, we compute local occurrences of an extreme dry or wet condition for a specified duration and return period, every year. A global exposure index is then derived for major mining commodities, by weighting extreme event occurrence by local production exposed. We note significant spatial and temporal clustering of exposure leading to the potential for fat tail risk associated with investment portfolios and supply chains. The traditional approach of climate risk analysis only considers local or point extreme value analysis and hence does not account for this spatially and temporally clustered exposure. Consequently, the global economic implications of the past or future financial and social exposure are understated in current climate risk analyses. [ABSTRACT FROM AUTHOR]

The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: While historical and paleo climate records emphasize low‐frequency modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi‐periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N‐year historical record. The uncertainty of IPCC‐like future scenarios is considered through an equivalent sample size N. The relative importance of estimating short‐ or long‐term risk depends on the investment life M. Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation. Key Points: Quasi‐periodic and secular climate signals, with different identifiability and predictability, control future uncertainty and riskAdaptation strategies must consider how uncertainties in risk projections influence success of decision pathwaysStylized experiments reveal how bias and variance of climate risk projections influence risk mitigation over a finite planning period [ABSTRACT FROM AUTHOR]

Low‐flow events can cause significant impacts to river ecosystems and water‐use sectors; as such, it is important to understand their variability and drivers. In this study, we characterise the variability and timing of annual total frequency of low‐streamflow days across a range of headwater streams within the continental United States. To quantify this, we use a metric that counts the annual number of low‐flow days below a given threshold, defined as the cumulative dry days occurrence (CDO). First, we identify three large clusters of stream gauge locations using a Partitioning Around Medoids (PAM) clustering algorithm. In terms of timing, results reveal that for most clusters, the majority of low‐streamflow days occur from the middle of summer until early fall, although several locations in Central and Western United States also experience low‐flow days in cold seasons. Further, we aim to identify the regional climate and larger scale drivers for these low‐streamflow days. Regionally, we find that precipitation deficits largely associate with low‐streamflow days in the Western United States, whereas within the Central and Eastern U.S. clusters, high temperature indicators are also linked to low‐streamflow days. In terms of larger scale, we examine sea surface temperature (SST) anomalies, finding that extreme dry years exhibit a high degree of co‐occurrence with different patterns of warmer SST anomalies across the Pacific and Northern Atlantic Oceans. The linkages identified with regional climate and SSTs offer promise towards regional prediction of changing conditions of low‐streamflow events. [ABSTRACT FROM AUTHOR]

Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS. [ABSTRACT FROM AUTHOR]

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]

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]

The author reflects on the hydrologic sciences and suggests a path forward on water-related matters that transcend some of the traditional boundaries of hydrology. He states the fundamental role of all three phases of water in the organization of the thermodynamic equilibrium and the climate dynamics of the planet. He states that the General Circulation models of the ocean-atmosphere dynamics provide downscaled scenarios for use with hydrological models.

Twenty extreme spring floods that occurred in the Ohio basin between 1901 and 2008, identified from daily river discharge data, are investigated and compared to the April 2011 Ohio River flood event. Composites of synoptic fields for the flood events show that all of these floods are associated with a similar pattern of sustained advection of low-level moisture and warm air from the tropical Atlantic Ocean and the Gulf of Mexico. The typical flow conditions are governed by an anomalous semistationary ridge, situated east of the U.S. East Coast, that steers the moisture and converges it into the Ohio River valley. Significantly, the moisture path common to all of the 20 cases studied here as well as the case of April 2011 is distinctly different from the normal path of Atlantic moisture during spring, which occurs farther west. It is shown further that the Ohio basin moisture convergence responsible for the floods is caused primarily by the atmospheric circulation anomaly advecting the climatological mean moisture field. Transport and related convergence due to the covariance between moisture anomalies and circulation anomalies are of secondary but nonnegligible importance. The importance of atmospheric circulation anomalies to floods is confirmed by conducting a similar analysis for a series of winter floods on the river Eden in northwest England. [ABSTRACT FROM AUTHOR]

The article suggests a modified support vector machine (SVM) based prediction model for the improvement of the predictability of the inflow to Shihmen Reservoir in Taiwan. In building the proposed framework, highly correlated climate precursors are identified and adopted to predict water availability and genetic algorithm based parameter determination procedure is enforced to the SVM parameters to know the non-linear patterns in climate systems. Bagging is then applied to create the models.

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility. [ABSTRACT FROM AUTHOR]

The article presents an analysis and interpretation of global sea surface temperature (SST) data using independent component analysis (ICA) and principal component analysis (PCA). It states that PCA represents multivariates and expands the variance explicated by components. It compares the capacity of PCA, the Varimax rotation and ICA in demonstrating climate unpredictability in SST anomaly (SSTA) data distributed worldwide.

Using a 1951–2003 gridded daily rainfall dataset for India, the authors assess trends in the intensity and frequency of exceedance of thresholds derived from the 90th and the 99th percentile of daily rainfall. A nonparametric method is used to test for monotonic trends at each location. A field significance test is also applied at the national level to assess whether the individual trends identified could occur by chance in an analysis of the large number of time series analyzed. Statistically significant increasing trends in extremes of rainfall are identified over many parts of India, consistent with the indications from climate change models and the hypothesis that the hydrological cycle will intensify as the planet warms. Specifically, for the exceedance of the 99th percentile of daily rainfall, all locations where a significant increasing trend in frequency of exceedance is identified also exhibit a significant trend in rainfall intensity. However, extreme precipitation frequency over many parts of India also appears to exhibit a decreasing trend, especially for the exceedance of the 90th percentile of daily rainfall. Predominantly increasing trends in the intensity of extreme rainfall are observed for both exceedance thresholds. [ABSTRACT FROM AUTHOR]

Results from diagnostic analyses to understand the seasonal evolution of the large-scale climatic state responsible for the development and melt of the winter snowpack, and spring–early summer precipitation in the Churchill Falls region on the Québec-Labrador Peninsula, Canada, are presented in the context of the development of an empirical model for seasonal to annual streamflow forecasting, with a special emphasis on the May–July spring freshet. Teleconnection indices and gridded global measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis are used as climatic indicators. Composite and correlation analyses are applied to the climatic indicators conditioned on the spring streamflow for identification of potential predictors. Meridional and zonal atmospheric fluxes over the Atlantic and the Pacific Oceans emanating from regionally persistent sea surface temperature/sea level pressure modes are identified as potential carriers of information. We speculate on the ocean-atmosphere and regional hydrologic mechanisms that may be involved in lending multiseasonal predictability to streamflows in the region. [ABSTRACT FROM AUTHOR]

The performance of different models and procedures for forecasting aggregated May–July streamflow for the Churchill Falls basin on the Québec-Labrador peninsula is compared. The models compared have different lead times and include an autoregressive model using only past streamflow data, an autoregressive with exogenous input model utilizing both past streamflow and precipitation, and a linear regression model using the principal components of exogenous measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The forecast skills of the different approaches are compared using a variety of measures of performance. The results indicate that relatively accurate forecasts using only measures of atmospheric circulation can be issued as early as in December of the prior year. A multimodel combination approach is found to be more effective than the use of a single forecast model. In addition, it is concluded that forecasting models utilizing atmospheric circulation data are useful, especially for basins where hydroclimatic observations are scarce and for basins where flows and other hydroclimatic variables are not strongly autocorrelated (do not depend on their past). [ABSTRACT FROM AUTHOR]

An improved Bayesian optimal weighting scheme is developed and used to combine six atmospheric general circulation model (GCM) seasonal hindcast ensembles. The approach is based on the prior belief that the forecast probabilities of tercile-category precipitation and near-surface temperature are equal to the climatological ones. The six GCMs are integrated over the 1950–97 period with observed monthly SST prescribed at the lower boundary, with 9–24 ensemble members. The weights of the individual models are determined by maximizing the log likelihood of the combination by season over the integration period. A key ingredient of the scheme is the climatological equal-odds forecast, which is included as one of the “models” in the multimodel combination. Simulation skill is quantified in terms of the cross-validated ranked probability skill score (RPSS) for the three-category probabilistic hindcasts. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared. The Bayesian optimal weighting scheme outperforms the pooled ensemble, which in turn outperforms the individual models. In the extratropics, its main benefit is to bring much of the large area of negative-precipitation RPSS values up to near-zero values. The skill of the optimal combination is almost always increased (in the large spatial averages considered) when the number of models in the combination is increased from three to six, regardless of which models are included in the three-model combination. Improvements are made to the original Bayesian scheme of Rajagopalan et al. by reducing the dimensionality of the numerical optimization, averaging across data subsamples, and including spatial smoothing of the likelihood function. These modifications are shown to yield increases in cross-validated RPSS skills. The revised scheme appears to be better suited to combining larger sets of models, and, in the future, it should be possible to include statistical models into the weighted ensemble without fundamental difficulty. [ABSTRACT FROM AUTHOR]

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a “prior” forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or “regularized” categorical probabilities. The combination algorithm assigns a weight to a particular model forecast and to climatology. The ratio of the sample likelihood of the model based on the posterior categorical probabilities, to that based on climatological probabilities, computed over the period of record of historical forecasts, provides a measure of the skill or information content of a candidate model. The weight given to a GCM forecast serves as a secondary indicator of its information content. Model weights are determined by maximizing the likelihood ratio. Results using the so-called ranked probability skill score as an objective function are also obtained, and are found to be very similar to the likelihood-based results. The procedure is extended to the optimal combination of forecasts from multiple GCMs. An application of the method is presented for global, seasonal precipitation and temperature forecasts in two different seasons, based on 41 yr of observational and model simulation data. The multimodel combination skill is significantly better than climatology skill in only a few regions of the globe, but is generally an improvement over individual models, and over a simple average of forecasts from different models. Limitations and possible improvements of the methodology are discussed. [ABSTRACT FROM AUTHOR]

Historical variations in the equator-to-pole surface temperature gradient (EPG) and the ocean--land surface temperature contrast (OLC) based on spatial finite differencing of gridded historical sea surface and land air temperatures are analyzed. The two temperature gradients represent zonally symmetric and asymmetric thermal forcings of the atmosphere. The strength and position of the Hadley cell and of the westerlies is related to the EPG, while the strength of the eddies coupled to the mid/high-latitude quasigeostrophic flow is related to the OLC. Taking these two parameters as simple yet highly meaningful diagnostics of the low-frequency variability of the atmosphere and climate system, the authors revisit a number of timely issues in the area of diagnostic climate studies. Of particular interest are seasonality and its variations and evidence of warming expected from greenhouse gas increases. Investigations of possible effects of CO2 -induced greenhouse warming are pursued by comparing the trends in EPG and OLC estimated from the observations and by using the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) results for control and transient-increased CO2 simulations. Significant differences are noted between the trends in EPG and OLC for observational data and the increased CO2 GCM scenario. However, the dynamical response of both EPG and OLC during subperiods with warming and cooling is consistent with that exhibited by the GFDL GCM. In this sense, the "fingerprint" of anthropogenic forcing of the climate is not clearly evident in these basic diagnostics of large-scale climate variability. [ABSTRACT FROM AUTHOR]

Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasi-oscillatory eigenmodes with characteristic timescales and large-scale spatial patterns of coherence. It is assumed that any such modes are superposed on a spatially and temporally autocorrelated stochastic noise background. Under such a paradigm, a previously described (Mann and Park) multivariate frequency-domain approach is promoted as a particularly effective means of spatiotemporal signal identification and reconstruction, and an associated forecasting methodology is introduced. This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm. The example application demonstrates statistically significant skill at 5-10-yr lead times. Applications to operational long-range climatic forecasting are motivated and discussed. [ABSTRACT FROM AUTHOR]