Extended range streamflow forecasts

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Extended Range Streamflow Forecasts: Techniques and Applications

Introduction to Extended Range Streamflow Forecasts

Extended range streamflow forecasts are crucial for water resource management, flood early warning systems, and drought-risk assessment. These forecasts, which predict streamflow over weeks to months, rely on various modeling techniques and climate indicators to provide accurate and reliable predictions.

Statistical Models for Intermittent Streamflow

In semi-arid regions like South Western Queensland, Australia, forecasting intermittent streamflow presents unique challenges due to the non-linear and non-stationary nature of the data. A statistical model using Generalised Additive Models for Location, Scale, and Shape (GAMLSS) has been applied to forecast streamflow up to 12 months ahead. This model incorporates logistic regression to predict the probability of flow and uses the Box-Cox t distribution to model the intensity of non-zero streamflows. Key covariates include time, seasonality, and climate indices related to sea surface temperatures, which significantly influence flow variability.

Ensemble Streamflow Prediction Systems

Ensemble streamflow prediction (ESP) systems are essential for reservoir operations and flood early warning. In the Narmada River Basin, India, the Extended Range Forecast System (ERFS) and Global Ensemble Forecast System (GEFS) have been used to predict streamflow. The GEFS, with its 21 ensemble members, has shown better performance than the ERFS, particularly for short-term forecasts up to 5 days. These systems help in developing reliable flood early warning systems by considering the influence of reservoirs.

Conditioning Climatology with Precipitation Indices

Conditioning historical climatology with seasonal precipitation indices can improve the sharpness and reliability of streamflow forecasts. This method involves selecting relevant historical time series based on general circulation model (GCM) outputs. Studies have shown that conditioning on seasonal precipitation indices enhances forecast sharpness but may reduce reliability compared to unconditioned climatology. This approach is particularly useful for low-flow event detection and drought-risk forecasting.

Climate Predictors in Multivariate Models

Large-scale climate indicators, such as surface precipitation rate and pressure vertical velocity, can significantly improve long-range streamflow forecasts. In the Lake Urmia basin, Iran, multivariate models that consider cross-correlations among different stations have shown better performance than univariate models. These models use principal component analysis and adaptive-network-based fuzzy inference systems to forecast streamflow, demonstrating the importance of incorporating climate predictors.

Deep Learning for Long-Term Forecasts

Deep learning algorithms, particularly long short-term memory (LSTM) and convolutional neural network (CNN) Encoder-Decoder models, have shown promise in improving long-term reservoir inflow forecasts. These models, trained on historical snow water equivalent (SWE) and inflow data, have outperformed traditional statistical methods and rivaled process-based models. The LSTM Encoder-Decoder algorithm, in particular, has proven effective for predicting inflow volumes during critical runoff periods.

Time-Varying Parameter Models

Time-varying parameter (TVP) models are useful for extended streamflow forecasting by capturing system recurrence information. These models assume that river flow can be represented by superstatistical random walks and use a multi-level modeling framework to produce reliable forecasts. TVP models can incorporate exogenous descriptors and are applicable to various forecasting scenarios, including flood risk mitigation.

Nonparametric Approaches

Nonparametric statistical procedures can combine hydrologic models and historical data to produce long-range streamflow forecasts. These methods, used within the National Weather Service River Forecast System (NWSRFS), allow for the incorporation of climate information and accommodation of model errors. They have been successfully applied in regions like the southeastern United States during severe drought periods.

Persistence-Based Forecasting

Persistence-based forecasting leverages real-time streamflow observations to predict future flows. This method, which assumes that current streamflow conditions will persist, has shown varying skill depending on basin size and river network topology. It is particularly effective for short-term forecasts and can be extended to space through flow-connected river networks.

Conclusion

Extended range streamflow forecasts are vital for effective water resource management and disaster preparedness. Various techniques, from statistical models and ensemble prediction systems to deep learning and nonparametric approaches, offer different strengths and applications. By leveraging climate predictors and advanced modeling frameworks, these forecasts can provide valuable insights for managing water resources and mitigating flood and drought risks.