2. MATERIALS AND METHODS
3.3 Evaluation of water level ensemble predictions
122 those with bias correction. However, the predictions overestimate the frequency of floods for all lead times and for all thresholds.
Figure 14: Same as Fig.12, for stations in the lower Madeira basin.
123 hydrological models do, but also the water level and the extension of inundated areas. Even though the discharge and the water level are closely related, river level depends more on the local geomorphology such as the river cross section. Water level simulations by MGB forced with the precipitation data from RCM-Eta, with and without bias correction, are evaluated for nine stations.
Due to lack of absolute reference data for the stations in the basin, the anomalies of the observed and simulated datasets are obtained by subtracting the respective long term means (Paiva et al., 2013). The evaluations of the simulations are shown in Fig. 15, as well as through Taylor diagram in Fig. 16 for the nine selected stations.
Overall, the model performance is satisfactory for all analyzed sub-basins. The river level graphs presented in Fig. 15 show the progression of the flood peaks and drought minima along the basin. The simulation of Eta-MGB-RAW overestimated the maxima and underestimated the minima in the water level variability, mainly in the sub-basins with larger catchment areas such as Cachuela Esperanza, Porto Velho, Humaitá and Manicoré. However, the predictions with bias corrected precipitation data show improvements in the forecasts of the peaks and minima.
Performance metrics of the predictions are satisfactory, presenting SD and CC values near unity and smaller values for CRMSE (Fig. 16). The EQM and PQM bias correction methods yield predictions closer to the observational data.
124 Figure 15: Water (River) level anomaly. Gray: observation data. Black: Eta-MGB-RAW simulated raw.
Bias-corrected Eta-MGB-EQM in red. Bias-corrected Eta-MGB-PQM in blue. Bias-corrected Eta-MGB-LS in green.
Figure 16: Taylor diagram showing correlation, centered mean square error and the ratio between the standard deviations of the simulated and observed river levels at nine stations distributed in the basin. The station locations are shown in Fig. 1. Color designation of the dots is shown in the inset. Linear distance from the green square mark is proportionate to CRMSE.
125 The performance of MGB forced by the outputs from RCM-Eta with and without bias correction in the representation of the extension of inundated area in the Madeira basin is shown in Fig. 17. The simulations are compared with GIEMS-2 observational data. Maximum inundated area of > 39.000 km² occurred in the period February through April and the minimum occurred in the trimester August to October. The MGB represents the seasonality of inundated area satisfactorily.
The simulations with bias corrected data presented improvements over the simulations with raw data. The predictions from Eta-MGB_RAW underestimate the inundated area for the whole period, while the predictions from Eta-MGB_EQM are closer to the GIEMS-2 data.
Figure 17: Monthly climatology of the inundated area in the whole basin. Gray line: GIEMS-2 data.
Ensemble simulation without bias correction: black line. After three methods of bias correction: Eta-MGB-EQM in red, Eta-MGB-PQM in blue and Eta-MGB-LS in green. The blue shading represents the standard deviation.
4. SUMMARY AND CONCLUSIONS
This work presents an estimation of the potential ability of intraseasonal discharge forecasts
126 for different probability categories over the largest sub-basin of the Amazon. Raw and bias corrected precipitation given by RCM-Eta are used as input for the MGB coupled hydrological-hydrodynamic model to produce ensemble of discharge forecasts. The model is validated and calibrated with the help of observed data at some select stations along the Madeira River and its tributaries, a region prone to floods and droughts. Forecasts obtained by with the raw precipitation data were able to capture the seasonality of high and low discharges, as well inundated areas with high values of temporal correlations. However, these forecasts showed large uncertainties.
The performance of the hydrometeorological forecast system varied spatially, in time and with lead time. Number of consecutive days with reliable forecasts, ability to discriminate low, moderate and high discharges in intraseasonal scale vary from place to place. All the performance metrics and quality criteria indicated that the forecasts before and after bias correction are reliable up to 30-days lead time. However, pre-processing of the meteorological data fed to the hydrological model can improve the discharge, river level and inundated area forecasts. Improvements are better in low and moderate river flow situations for shorter lead times. Bias correction in the precipitation data is always recommended. The pre-processing procedure not only removes or reduces bias, but also allows us to quantify the improvements in the quality of forecasts.
We show that the ability of the forecasts to discriminate low, medium and high discharge episodes is dependent on the size of the sub-basin. For some sub-basins the discriminating ability diminished with catchment area, even for longer lead times. The hit rate for the ROC curves is greater than 70% which is considered adequate for the decision makers. The forecast system developed here, i.e., the models, their coupling, the bias correction schemes and statistical evaluations combined, shows its potential to obtain reliable forecasts in the Madeira basin.
Coughlan de Perez et al. (2015) suggested that an ensemble system that produces less than 50%
false alarms helps the decision makers in terms of economic consequences. In all conditions of low,
127 moderate and high discharge situations the CRPSS values for smaller basins are smaller than for larger basins. This is perhaps an indication that the uncertainties in the meteorological parameters are larger than the uncertainties in the hydrological parameters. Our results suggest that large basin hydrology is affected more by the initial condition than the predicted rain and the predicted rain becomes important after a sufficiently longer time. The propagation of inundation is slow in basins with gentle slopes as is observed in the Madeira basin.
Our findings emphasize also the usefulness of dynamic downscaling and statistical procedure utilized in this study for forecasts along an important tributary in the Amazon basin, producing skillful forecasts of discharge, river level and inundated area for lead times up to 30 days.
Considering a basin where observations and forecasts are scanty, the intraseasonal time scale forecasts can certainly improve the decision-making process in many spheres such as flood and drought warning. Useful forecasts of imminent floods or inundations should leave enough time for the warning to reach the people that will be eventually affected. This work emphasizes not only the necessity of performance evaluation against observations but also quantifying the uncertainties in the forecasts. The results obtained and explanations and interpretations provide sufficient evidence for building confidence in the forecast system for floods and droughts as a guiding tool for decision making and can be integrated into existing operational systems.
The main results of the study and conclusion are: (1) The Eta+MGB ensemble predictions of the hydrological parameters for the Madeira basin, in general, show good performance; (2) the model performance is not uniform in the basin, showing differences in performance in the upper, middle and lower portions of the basin; (3) removal or reduction of systematic errors or biases in the meteorological data before using them to force a hydrological model greatly improves the performance as seen from statistical metrics; (4) among the three schemes of bias correction utilized, EQM performs better; (5) the model has the capacity to discriminate between high,
128 medium and low streamflow situations; (6) the model performance is better for sub-basins with larger catchment areas; (7) the hit rates exceed the false alarms in all probability ranges, approximately in the ratio of 70 to 20. In summary, we conclude that the ensemble prediction system developed here, coupling the regional atmospheric model RCM-Eta with the large-scale MGB hydrological model, can potentially be used for operational forecasts of discharges, river level and inundated area in the Madeira River basin.
ACKNOWLEDGMENTS
This study was financed in part by the Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) - Finance Code: 01.02.016301.00268/2021. This work is developed in the Postgraduate Program in Climate and Environment (CLIAMB) jointly coordinated by the Amazon State University (UEA) and the National Institute of Amazonian Research (INPA). The first author thanks the FAPEAM for the doctoral grant. Prakki Satyamurty is supported by PVNS Grant No.
2308.019802/2018-7 from CAPES, Brazil and Research Productivity Grant no. 306486/2021-0 of CNPQ, Brazil. All authors thank the Center for Weather Forecasting and Climate Studies - National Institute of Space Research (CPTEC/INPE) for making available the numerical integrations datasets and the Laboratory of Terrestrial Climate System Modeling (LABCLIM/UEA) for providing the computational infrastructure - TAMBAQUI Cluster.
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141 Supporting Information for
Ensemble hydrological predictions at intraseasonal scale through a statistical-dynamical downscaling approach over southwestern Amazonia
Weslley de Brito Gomes a, Prakki Satyamurty a, Francis Wagner Silva Correia b, Sin Chan Chou c, Ayan Santos Fleischmannd, Fabrice Papae,f, Leonardo Alves Vergasta a, André de Arruda Lyra c.
a Postgraduate Program in Climate and Environment (CLIAMB, INPA/UEA), Av. André Araújo, 2936, Campus II, Aleixo, 69060-001, Manaus, Amazonas, Brazil.
b Amazonas State University, Superior School of Technology, Av. Darcy Vargas, 1200, Parque 10 de Novembro, 69065-020, Manaus, Amazonas, Brazil.
c National Institute for Space Research, Center for Weather Forecasting and Climate Research, Av. dos Astronautas, 1758, 12227-010, São José dos Campos, São Paulo, Brazil.
d Mamirauá Institute for Sustainable Development, Tefé, AM, Brazil.
e Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université Toulouse, IRD, CNRS, CNES, USP, Toulouse, France
f Institut de Recherche pour le Développement (IRD), University of Brasilia (UnB), Institute of Geosciences, Brasília, Brazil
Corresponding author: Weslley de Brito Gomes, [email protected]