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A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information

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Abstract

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world.

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Data used in this study are publicly available as indicated in the article and from authors.

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Acknowledgements

The authors thank the reservoir management sector of the Southern Company—Alabama Power for providing the inflow data of the Harris reservoir and Ms. Tayler Schillerberg for her assistance in processing the GLEAM data.

Funding

This research was supported in part by the USGS Alabama Water Resources Institute 104(b) Annual Grant Program, by the Auburn University Intramural Grant Program, and by the Alabama Agricultural Experiment Station and the Hatch Program of the National Institute of Food and Agriculture, US Department of Agriculture.

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Authors and Affiliations

  1. Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, Alabama, USA

    Di Tian

  2. Water in the West, Stanford University, Stanford, California, USA

    Xiaogang He

  3. College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA

    Puneet Srivastava

  4. School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, USA

    Latif Kalin

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Tian, D., He, X., Srivastava, P. et al. A hybrid framework for forecasting monthly reservoir inflow based on machine learning techniques with dynamic climate forecasts, satellite-based data, and climate phenomenon information. Stoch Environ Res Risk Assess 36, 2353–2375 (2022). https://doi.org/10.1007/s00477-021-02023-y

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