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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-404
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-404
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Aug 2018

Research article | 20 Aug 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

Seasonal drought prediction for semiarid northeast Brazil: About the added value of a process-based hydrological model

Tobias Pilz1, José Miguel Delgado1, Sebastian Voß1, Klaus Vormoor1, Till Francke1, Alexandre Cunha Costa2, Eduardo Martins3, and Axel Bronstert1 Tobias Pilz et al.
  • 1Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
  • 2Institute of Engineering and Sustainable Development, University of International Integration of the Afro-Brazilian Lusophony (UNILAB), Acarape, Ceará, Brazil
  • 3Research Institute for Meteorology and Water Resources – FUNCEME, Fortaleza, Ceará, Brazil

Abstract. The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.

Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance towards rainfall forecast quality.

Based on the findings of this study, we recommend using a statistical approach for merely predicting reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model is worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.

Tobias Pilz et al.
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Short summary
This work investigates different model types for drought prediction in a dryland region. Consequently, the performances of seasonal reservoir volume forecasts derived by a process-based and a statistical hydrological model were evaluated. The process-based approach obtained lower accuracy while resolution and reliability of drought prediction were comparable. Initialisation of the process-based model is worthwhile for more in-depth analyses, provided adequate rainfall forecasts are available.
This work investigates different model types for drought prediction in a dryland region....
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