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SaTellite-based Run-off Evaluation And Mapping (STREAM)

CNR – INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT (IREA) (IT)

Summary

The STREAM Project (SaTellite based Runoff Evaluation And Mapping), led by CNR-IRPI with the participation of the Institute of Geodesy (GIS) at University of Stuttgart, aimed at developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies).

In situ observations of river discharge, used for the quantification of total runoff, typically offer little information on its spatial distribution within a watershed. Moreover, river discharge observation networks suffer from many limitations such as low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity. Paradoxically, this issue is exacerbated in poor non-industrialized nations where the knowledge of the terrestrial water dynamics is even more important. On the other hand, land surface and hydrological models are very highly data demanding, based upon complex modelling systems and might suffer from an incorrect representation of the pre-storm condition, which is paramount for a proper runoff estimation

In this context, the STREAM project aimed at:

  1. Investigate the possibility to use satellite data for the hydrological cycle modeling; and
  2. developing a conceptual hydrological model, STREAM, directly ingesting satellite observation of soil moisture (SM), precipitation (P) and terrestrial water storage anomalies (TWSA).

The goal of the project was to estimate runoff and river discharge time series for large basins in the world at high spatial and temporal resolution. During the 12 months of project activity, a quality assessment of STREAM river discharge and runoff estimates was carried out over five basins (Mississippi, Amazon, Danube, Niger and Murray-Darling). In these areas, the model was able to accurately simulate continuous daily river discharge and total runoff time series for the period 2003-2016. Only for specific case studies, such as for basins with high human impact or for highly vegetated areas, unsatisfactory model performances were found.

To address this issue, the project activity has been extended of 1 year through a CCN (STREAMRIDE) to explore the possibility both to improve the STREAM model and to complement the model with a different satellite approach for river discharge estimation (RIDESAT)


Scientific Papers

  • Synergy between satellite observations of soil moisture and waterstorage anomalies for runoff estimation

    Camici, S., Giuliani, G., Brocca, L., Massari, C., Tarpanelli, A.,Farahani, H. H., Sneeuw, N., Restano, M., and Benveniste, J.

    Geoscientific Model Development (2022)

     

    This paper presents an innovative approach, STREAM – SaTellite-based RunoffEvaluation And Mapping, to derive daily river discharge andrunoffestimates from satellite observations of soil moisture,precipitation,and total water storage anomalies (TWSAs). Despite a very simplemodelstructure, by directly ingesting observations of soil moistureand totalwater storage data, STREAM model allows the modeler to neglectprocessesthat are implicitly accounted for in the input data andaccurately estimaterunoff and river discharge. Therefore, human-driven processes(e.g., irrigation,land use change), that are typically very difficult to model dueto missinginformation, and that might have a large impact on thehydrological cycle,and hence on runoff, are modeled implicitly.


  • Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

    Camici S., Giuliani G., Brocca L., Massari C., Tarpanelli A., Farahani H.H., Sneeuw N., Restano M., Benveniste J.

    Geoscientific Model Development (2022)


  • Sentinel-2 high-resolution data for river discharge monitoring

    Filippucci P., Brocca L., Bonafoni S., Saltalippi C., Wagner W., Tarpanelli A.

    Remote Sensing of Environment (2022)


Information

Website
http://hydrology.irpi.cnr.it/projects/stream
Domain
Science
Prime contractor
CNR – INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT (IREA) (IT)
Subcontractors
  • Technical University of Denmark (DK)
  • UNIVERSITY OF STUTTGART – INSTITUTE OF GEODESY (DE)