Short- and mid-term discharge forecasts combining machine learning and data assimilation for operational purpose

In recent years, machine learning models, particularly Long Short-Term Memory (LSTM), have proven to be effective alternatives for rainfall-runoff modeling, surpassing traditional hydrological modeling approaches ¹. These models have predominantly been implemented and evaluated for rainfall-runoff simulations. However, operational hydrology often requires short- and mid-term forecasts. To be effective, such forecasts must consider past observed values of the predicted variables, requiring a data assimilation procedure ^2,3,4. This presentation will evaluate several approaches based on the combination of open-source machine learning tools and data assimilation strategies for short- and mid-term discharge forecasting of flood and/or drought events. The evaluation is based on the rich and well-documented CAMELS dataset ^5,6,7. The tested approaches include: (1) coupling pre-trained LSTMs on the CAMELS database with a Multilayer Perceptron (MLP) for prediction error corrections, (2) direct discharge MLP forecasting models specific for each lead time, including past observed discharges as input variables, and (3) option 2, including the LSTM-predicted discharges as input variables. In the absence of historical archives of weather forecasts (rainfall, temperatures, etc.), the different forecasting approaches will be tested in two configurations: (1) weather forecasts assumed to be perfect (using observed meteorological variables over the forecast horizon in place of predicted variables or ensembles) and (2) use of ensembles reflecting climatological variability over the forecast horizons for meteorological variables ensembles made up of time series randomly selected from the past. The forecast horizons considered range from 1 to 10 days, and the results are analyzed in light of the time of concentration of the watersheds.

 

References

1. Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol Earth Syst Sci. 2018;22(11):6005-6022. doi:10.5194/hess-22-6005-2018

2. Bourgin F, Ramos MH, Thirel G, Andréassian V. Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting. J Hydrol (Amst). 2014;519:2775-2784. doi:10.1016/j.jhydrol.2014.07.054

3. Boucher M ‐A., Quilty J, Adamowski J. Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons. Water Resour Res. 2020;56(6). doi:10.1029/2019WR026226

4. Piazzi G, Thirel G, Perrin C, Delaigue O. Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale. Water Resour Res. 2021;57(4). doi:10.1029/2020WR028390

5. Newman AJ, Clark MP, Sampson K, et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrol Earth Syst Sci. 2015;19(1):209-223. doi:10.5194/hess-19-209-2015

6. Kratzert, F. (2019). Pretrained models + simulations for our HESSD submission "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets", HydroShare, https://doi.org/10.4211/hs.83ea5312635e44dc824eeb99eda12f06

7. Kratzert, F. (2019). CAMELS Extended Maurer Forcing Data, HydroShare, https://doi.org/10.4211/hs.17c896843cf940339c3c3496d0c1c077