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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #345330

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: An assessment of the accuracy of global rainfall estimates without ground-based observations

item MASSARI, C. - National Center For Agriculture And Forestry Technologies (CENTA)
item Crow, Wade
item BROCCA, L. - National Center For Agriculture And Forestry Technologies (CENTA)

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2017
Publication Date: 12/1/2017
Citation: Massari, C., Crow, W.T., Brocca, L. 2017. An assessment of the accuracy of global rainfall estimates without ground-based observations. Hydrology and Earth System Sciences. 21:4347-4361.

Interpretive Summary: Agricultural drought represents the single most important environmental impact on agricultural productivity. Timely estimates of daily rainfall accumulation over land form the backbone of all agricultural drought monitoring systems. However, these estimates must be validated before they can be used with confidence. Such validation is often impossible due to spatial gaps in the availability of high-quality, rain-gauge observations (to serve as an objective source of ground truth). This paper describes a new approach for validating rainfall accumulation estimates without reliance on ground-based observations. The key to this approach is the generation of a new, wholly-independent, daily rainfall accumulation product derived from the inversion of satellite-based surface soil moisture retrievals and the application of a mathematical tool called “Triple Collocation". When combined, these two innovations allow us to objectively evaluate the quality of land rainfall products for any point on the globe. This approach will eventually be used to improve our ability to globally monitor (and therefore mitigate) the impact of drought on agriculture.

Technical Abstract: Satellite-based rainfall estimates have great potential value for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of Triple Collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated products of this variable. However, TC requires the simultaneous availability of three products with mutually-uncorrelated errors, a requirement that is difficult to satisfy among current global precipitation datasets. In this study, a recently-developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high quality ground rainfall product over the Contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (versus an unknown truth) of the aforementioned products without using a ground benchmark dataset. The analysis is carried out during the period 2012-2015 using daily rainfall accumulation products obtained at 1_x1_ spatial resolution. Results convey the relatively high accuracy of the satellite rainfall estimates in Eastern North and South America, South Africa, Southern and Eastern Asia, Eastern Australia as well as Southern Europe and complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the northern hemisphere and the second providing very good performance in the southern hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.