Abstract
Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertainty in environmental analyses, attributable to the choices made by data-users in selecting a representation of rainfall. We use the rainforest-savanna transition region in Brazil to show differences in the statistics describing rainfall across nine RS and interpolated-IS daily rainfall datasets covering the period of 1998–2013. These differences propagate into estimates of temporal trends in monthly rainfall and descriptive hydroclimate indices. Rainfall trends from different datasets are inconsistent at river basin scales, and the magnitude of index differences is comparable to the estimated bias in global climate model projections. To address this uncertainty, we evaluate the correspondence of different rainfall datasets with streamflow from 89 river basins. We demonstrate that direct empirical comparisons between rainfall and streamflow provide a method for evaluating rainfall dataset performance across multiple areal (basin) units. These results highlight the need for users of rainfall datasets to quantify this “data selection uncertainty” problem, and either justify data use choices, or report the uncertainty in derived results.
Highlights
Quantifying precipitation patterns at regional scales is essential for water security[1, 2], but is compromised by discrepancies in rainfall datasets[3,4,5]
For the purposes of this paper, we focus on the quantification of regional daily rainfall statistics needed for hydrologic analyses
This illustrates that rainfall detection, to which RS data errors are principally attributed[3], and representation of extremes, which vary with the level of spatial aggregation and due to interpolation method[13, 14], differentiate datasets
Summary
Quantifying precipitation patterns at regional scales is essential for water security[1, 2], but is compromised by discrepancies in rainfall datasets[3,4,5]. Spatial rainfall data products have proliferated, drawing on differing information sources, using different techniques to impute that information through space, and varying in their spatial extent and spatio-temporal resolution[6] The proliferation of such rainfall datasets facilitates applied research at regional spatial scales, but raises the risk that naïve use of an individual rainfall product may introduce bias into subsequent analyses, relative to the full range of representations of the rainfall field available[7]. Rainfall in center-west and northern Brazil is monitored through a relatively sparse rain gauge data network (15 or fewer rain gauges per 104 km2), comparable to inland regions of South America; sub-Saharan Africa; and central, east, and southeast-Asia[25] These low densities are likely to result in non-trivial differences between regional rainfall data products[26] (in Switzerland, rain gauge densities of >24 rain gauges per 1,000 km[2] were required to avoid density-dependent biases[9])
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