Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: Considering effects of different gauge densities

• Published in: Journal of hydrology (Amsterdam) Vol. 577; p. 124007
• Main Authors: Bai, Xiaoyan, Wu, Xiaoqing, Wang, Peng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2019
• Subjects: Drought monitoring; Gauge density; Geographical difference analysis (GDA); PERSIANN-CDR; Satellite Precipitation Estimate (SPE); SPE-gauge blending; SPE-gauge blending; PERSIANN-CDR; Geographical difference analysis (GDA); Gauge density; Satellite Precipitation Estimate (SPE); Drought monitoring
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2019.124007

•SPE-gauge blending can significantly improve the drought monitoring utility of SPEs.•Impacts of different gauge densities on SPE-gauge blending are investigated.•Improvements of blending are different for different aspects in drought monitoring.•Medium gauge density is most ideal for SPE-gauge blending in drought monitoring. Blending satellite-based precipitation estimation (SPE) data and in-situ gauge observation data can generate effective spatially-continuous-precipitation estimates with improved accuracy. This study assessed the improvement of the long-term SPE when blending with in-situ gauge observations for drought monitoring, using a simple but effective blending method named the geographical difference analysis (GDA) method and with the Precipitation Estimation from Remote Sensed Information by using Artificial Neural Networks-Climate Data Records (PERSIANN-CDR) as case study. In-situ precipitation observations from three meteorological station sets with different densities—the sparse (50), medium (200), dense (727) station set—were adopted to evaluate the effect of gauge density on the performance of SPE-gauge data blending. Two widely-used indices—standardized precipitation index (SPI) and self-calibrating Palmer drought severity index (SC_PDSI)—were used as case studies. Except the case of sparse 50-station subset, the SPE-gauge blending shows apparent improvement to the raw PERSIANN-CDR data, for both the accuracy of precipitation input and many aspects of drought monitoring, e.g. reproducing drought magnitude and revealing spatial pattern of drought, in which SC_PDSI shows more significant improvement than SPI. The dense 727-station set shows the largest improvement in the blending data, but the corresponding station-only interpolations also exhibit comparable performance to the blending data, indicating lower utilization value of the SPE data for these cases. Only the blending results of the medium-density 200-station set shows satisfactory drought monitoring performance as well as significant improvements relative to the station-only interpolations. According to the quantitative analyses, the medium density (about 50–75 gauges per 106km2 in our cases) might be the most economic gauge density for SPE-gauge blending, as it has satisfactory improvement in blending results, can make fullest use of the advantages of SPE data and requires relatively fewer gauges. Our results can help to understand how the SPE-gauge blending could improve the SPE-based drought monitoring and serves as a reference for applying drought monitoring under the data-limited conditions. Subsequent studies or applications should also carefully consider the effect of gauge density.