EGU22-7162, updated on 09 Jan 2023
EGU General Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Making the best of little information: operational forecasting and early warning systems in a data-scarce environment, the Beni River watershed in Bolivia.

Alessandro Masoero, Andrea Libertino, Matteo Darienzo, Simone Gabellani, and Lauro Rossi
Alessandro Masoero et al.
  • Fondazione CIMA, Hydrology and Hydraulics, Savona, Italy (

Implementing hydrological models in data-scarce watersheds involves several critical issues, especially in relation to the availability and reliability of input data. This becomes particularly challenging when dealing with real-time hydrological applications for EW purposes (e.g., flood forecasting chains) where input data should be up-to-date and reliable, to provide timely warnings and drive trustworthy early actions.

When local data are available, those are often collected with inadequate frequency and continuity and cannot be used for proper calibration, configuration and subsequent operational use of the hydrological model underpinning a flood forecast chain. Furthermore, the lack of information reduces knowledge and awareness of risk and increases the vulnerability of these data-scarce areas to water-related disasters. It is therefore of utmost importance to build reliable EWS for these watersheds, making the best of what (little) is available.

The combined use of satellite observations and innovative hydrometeorological data processing can be a practical solution to integrate and enhance local observations, improving the hydrological model performance in poorly gauged watersheds.

This approach has been applied to the upstream portion of the Beni River in Bolivia (Alto Beni, closing at Rurrenabaque, 70’000 km2), an Amazon River tributary originating from the Andes. The Flood-PROOFS forecasting chain, based on the CONTINUUM hydrological model (Silvestro, 2013) has been implemented on the Alto Beni together with SENAMHI (Hydrometeorological Service) and VIDECI (Civil Defence).

Despite the large size of the watershed and its socio-economic importance (hosting several riverine communities and representing a main connection route between Bolivian Altiplano and Amazon plain) few water-level and weather stations are available and in operation in Alto Beni. This scarcity of information, particularly to feed the Flood-PROOFS chain, can be mitigated by using satellite data and by complementing available local data with additional analyses.

To test the approach and select the best available data source, the hydrological model reconstruction of the 2014 event, the highest on records, has been performed comparing different remote-sensed rainfall inputs: GSMaP, IMERG, MSWEP, PERSIANN, GHE. Performance of each input in reproducing the 2013-2014 rainy season hydrograph at Rurrenabaque has been evaluated. GSMaP and MSWEP performed the best, yet with a non-negligible underestimation of discharge values. Moreover, none of the rainfall inputs was able to reconstruct the double peak shape of the 2014 event. The uncertainty in the rating curve, lacking regular updates and high flow records, should be also considered.

To address these issues two innovative data processing approaches have been undertaken: firstly, the level-discharge relation at Rurrenabaque has been revised, using an innovative approach (BayDERS, Darienzo 2021) to review the rating curve and update the discharge timeseries. Then, the satellite rainfall inputs have been integrated with the available ground weather station records, using an innovative conditional merging technique (GRISO, Bruno 2021).

After having performed these two local data enhancement techniques, the combination of GSMaP and ground stations demonstrated to perform the best in reproducing the 2014 event. Moreover, GSMaP, given its near-real-time availability, is a solid data source to feed the operational flood forecasting and EWS for the Alto Beni.

How to cite: Masoero, A., Libertino, A., Darienzo, M., Gabellani, S., and Rossi, L.: Making the best of little information: operational forecasting and early warning systems in a data-scarce environment, the Beni River watershed in Bolivia., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7162,, 2022.


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