Addressing rainfall data selection uncertainty using connections between rainfall and streamflow

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.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

“…Global gridded P products are typically much coarser than those available in North America and Europe and coarser than the MODIS-derived SP. These products are Water Resources Research 10.1002/2017WR021899 modeled and interpolated from station, radar and satellite data and can have considerable uncertainties (Bosilovich et al, 2008;Fekete et al, 2004;Kidd et al, 2012;Levy et al, 2017;Schamm et al, 2014) with heightened uncertainty of over 100% in high latitude and topographically complex areas including the Rocky Mountains, Tibetan Plateau, and Andes (Saavedra et al, 2018;Tian & Peters-Lidard, 2010).…”

Land–atmosphere interactions in the tropics – a review

“…Generally, the seasonality follows the variation in maximum solar heating, but other factors, such as ocean thermal inertia, topography, dynamics and circulation, moisture transport, and the state of the land surface, can exert considerable influence on the timing and amplitude of tropical land region seasonal evolution. Over the Amazon Basin, seasonality exhibits marked variation in both latitude and longitude: for example, at 5 • S, the dry-towet transition proceeds from the central Amazon eastward toward the Atlantic coast (Liebmann and Marengo, 2001). It is also worth noting a pervasive tendency for the dry-to-wet season transition to occur much more rapidly than the wetto-dry transition, as evident in tropical monsoon systems including South Asia, West Africa, and South America.…”

“…However, these authors also underscore the participation of the large-scale circulation and its role in establishing a background environment (e.g., moisture convergence) to support wet-season rainfall. Incursion of cold fronts into the southern Amazon may act as triggers for rapid initiation of wet-season onset once the local thermodynamics become favorable (Li et al, 2006).…”

Evaluation of Agricultural Land Cover Representations on Regional Climate Model Simulations in the Brazilian Cerrado

“…Although CRU data have been previously used as the observed data set of comparison in regional climate studies over Brazil (Da Rocha et al, 2015;Lee & Berbery, 2012), because there exists poor station data coverage in the interior of South America our "observed" data set may be biased and underestimate spatial and interannual variability (Malhi & Wright, 2004;New et al, 1999). In an analysis comparing various gridded precipitation data sets, including CRU, to Agencia Nacional de Aguas streamflow data, Levy et al (2017) demonstrated that over the Brazilian rainforest-savanna transition zone, which overlaps with our study region, the Precipitation Estimate from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) data set is the best observational data set for the region. In comparing the CRU data set to the PERSIANN data set, we find that the CRU data set closely aligns with the PERSIANN data set in Bahia, Piauí, Maranhão region, and Goiás and southwestern Mato Grosso ( Figure S4).…”