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Addressing rainfall data selection uncertainty using connections between rainfall and streamflow.

Morgan C Levy1, Avery Cohn2, Alan Vaz Lopes3

  • 1Energy and Resources Group, University of California, Berkeley, USA. mclevy@berkeley.edu.

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|March 18, 2017
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Summary
This summary is machine-generated.

Rainfall data products from satellites and gauges have biases, affecting hydroclimate studies. Comparing rainfall data with streamflow helps evaluate dataset performance and quantify data selection uncertainty.

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Area of Science:

  • Hydrology
  • Climate Science
  • Remote Sensing

Background:

  • Regional hydroclimate studies depend on spatial rainfall data from remote sensing (RS) and in-situ (IS) sources.
  • These data products are inherently biased due to the inability to directly measure regional rainfall, introducing uncertainty in environmental analyses.
  • Data selection by users is a significant source of this uncertainty.

Purpose of the Study:

  • To investigate differences in rainfall statistics across multiple RS and interpolated-IS daily rainfall datasets.
  • To assess how these differences impact estimates of rainfall trends and hydroclimate indices.
  • To evaluate methods for addressing data selection uncertainty in hydroclimate research.

Main Methods:

  • Analysis of nine RS and interpolated-IS daily rainfall datasets (1998-2013) in Brazil's rainforest-savanna transition zone.
  • Comparison of rainfall trends and hydroclimate indices derived from different datasets.
  • Evaluation of rainfall dataset performance by comparing them with streamflow data from 89 river basins.

Main Results:

  • Significant differences in rainfall statistics and trends were observed across the evaluated datasets.
  • Inconsistencies in rainfall trends were noted at river basin scales.
  • Empirical comparisons between rainfall and streamflow provided a robust method for evaluating dataset performance across various basin units.

Conclusions:

  • Data selection uncertainty is a critical issue in hydroclimate research using spatial rainfall products.
  • Users must justify their data choices or report the uncertainty associated with their findings.
  • Direct empirical validation against streamflow is recommended for assessing rainfall dataset reliability.