Probabilistic Streamflow Forecasts Research Articles

Systematic evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system

Summary In this study, different versions of autoregressive error models are evaluated as post-processors for probabilistic streamflow forecasts. The post-processors account for hydrologic uncertainties that are introduced by the precipitation–runoff model. The post-processors are evaluated with the discrete ranked probability score (DRPS), and a non-parametric bootstrap is applied to investigate the significance of differences in model performance. The results show that differences in performance between most model versions are significant. For these cases it is found that (1) error models with state dependent parameters perform better than those with constant parameters, (2) error models with an empirical distribution for the description of the standardized residuals perform better than those with a normal distribution, and (3) procedures that use a logarithmic transformation of the original streamflow values perform better than those that use a square root transformation.

Reliability of autoregressive error models as post-processors for probabilistic streamflow forecasts

Abstract. In this study, the reliability of different versions of autoregressive error models as post-processors for probabilistic streamflow forecasts is evaluated. Rank histograms and reliability indices are used as performance measures. An algorithm for the construction of confidence intervals to indicate ranges of reliable forecasts within the rank histograms is presented. To analyse differences in performance of the post-processors, scatter plots of the standardized residuals of the error models are generated to assess the homoscedacity of the residuals with respect to streamflow. A problem of distorted impressions may appear when such plots are generated with a regular x-scale. The problem is analysed with both synthetic and real data, and a rank scaled x-axis is proposed to remedy the problem. The results of the study reveal large differences in the reliability of the post-processors. Versions with empirical distribution functions are clearly superior to those with standard normal distribution, but for validations with independent data their rank histograms still lie outside of the confidence bands for reliable forecasts.

Effect of streamflow forecast uncertainty on real-time reservoir operation

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast ( DSF), DSF-based probabilistic streamflow forecast ( pseudo-PSF, pPSF), and ensemble or probabilistic streamflow forecast (denoted as real-PSF, rPSF). DSF represents forecast uncertainty in the form of deterministic forecast errors, pPSF a conditional distribution of forecast uncertainty for a given DSF, and rPSF a probabilistic uncertainty distribution. Compared to previous studies that treat the forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the dynamic evolution of uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. Through a hypothetical example of a single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases but the magnitude depends on the forecast products used. In general, the utility of the reservoir operation with rPSF is nearly as high as the utility obtained with a perfect forecast. Meanwhile, the utilities of DSF and pPSF are similar to each other but not as high as rPSF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, pPSF, and rPSF.

Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.

Evaluation of bias-correction methods for ensemble streamflow volume forecasts

Abstract. Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three bias-correction methods for ensemble streamflow volume forecasts. All three adjust the ensemble traces using a transformation derived with simulated and observed flows from a historical simulation. The quality of probabilistic forecasts issued when using the three bias-correction methods is evaluated using a distributions-oriented verification approach. Comparisons are made of retrospective forecasts of monthly flow volumes for a north-central United States basin (Des Moines River, Iowa), issued sequentially for each month over a 48-year record. The results show that all three bias-correction methods significantly improve forecast quality by eliminating unconditional biases and enhancing the potential skill. Still, subtle differences in the attributes of the bias-corrected forecasts have important implications for their use in operational decision-making. Diagnostic verification distinguishes these attributes in a context meaningful for decision-making, providing criteria to choose among bias-correction methods with comparable skill.

Long-Range Streamflow Forecasting Using El Niño-Southern Oscillation Indicators

Hydrologists and water resource planners in the Pacific Northwest region of the United States are under pressure from competing water users to make long-range forecasts of streamflow. At present, long-range streamflow forecasts are not offered. Yet in the Pacific Northwest, a significant lag relationship exists between (El Nino-SouthernOscillation) and streamflow. Using this lag relationship, this study proposes to extend the prediction of spring-summer runoff in the Pacific Northwest from the current 1- to 3-month lead time to a 3- to 7-month lead time. This study looks at the Columbia River basin, where testing is being conducted on a long-range seasonal streamflow forecasting model that uses, as predictors, persistence in streamflow along with two El Nino-SouthernOscillation indicators: the Southern Oscillation Index and the Wright sea surface temperatures. A probabilistic streamflow forecast is made from an optimal linear combination of persistence, Southern Oscillation Index linear discriminant analy...