Abstract
Abstract This study compares two nonparametric rainfall data merging methods—the mean bias correction and double-kernel smoothing—with two geostatistical methods—kriging with external drift and Bayesian combination—for optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical Andean watershed in Peru. The analysis is conducted using 11 years of daily time series from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (also TRMM 3B42) and 173 rain gauges from the national weather station network. The results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. It is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. The mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. Given the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. Based on these results, a systematic approach to the selection of a satellite–rain gauge data merging technique is proposed that is based on data characteristics. Finally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to TMPA and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.
Highlights
Hydrological studies rely on the quality of rainfall estimates to produce meaningful modeling output
Satellite-based estimates such as the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA; TRMM 3B42) product are becoming increasingly attractive as an alternative source of forcing data in data-sparse regions, their application
Information from a large number of rain gauges is already assimilated as part of global/regional satellite algorithms; the rain gauge databases sourced by these procedures can exclude more extensive national networks where data accessibility is restrictive, as often is the case in developing countries
Summary
Hydrological studies rely on the quality of rainfall estimates to produce meaningful modeling output. The rain gauge dataset is the historical rainfall time series obtained from the Peruvian National Meteorological and Hydrological Service [Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI)] This includes daily rainfall amounts in millimeters for 11 years between January 1998 and December 2008 from 173 recording stations located within the satellite domain. The kernel smoothing (interpolation) of the residuals does not rely on the stationary assumption, as is the case for geostatistical methods The formulation is such that the product of the merging will converge toward the rain gauge estimates with decreased distance toward the ground observations. The OK is used to produce the rainfall estimates at each satellite grid location through the interpolation of discrete (point or grid averaged) rain gauge measurements.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
