remote sensing Article Geostatistical Based Models for the Spatial Adjustment of Radar Rainfall Data in Typhoon Events at a High-Elevation River Watershed Keh-Han Wang 1, Ted Chu 1,2, Ming-Der Yang 3,4,* and Ming-Cheng Chen 3 1 Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204-4003, USA;
[email protected] (K.-H.W.);
[email protected] (T.C.) 2 CivilTech Engineering Inc., 11821 Telge Rd, Cypress, Houston, TX 77429, USA 3 Department of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan;
[email protected] 4 Pervasive AI Research (PAIR) Labs, HsinchU 300, Taiwan * Correspondence:
[email protected]; Tel.: +886-04-2284-0437 (ext. 214) Received: 1 April 2020; Accepted: 29 April 2020; Published: 1 May 2020 Abstract: Geographical constraints limit the number and placement of gauges, especially in mountainous regions, so that rainfall values over the ungauged regions are generally estimated through spatial interpolation. However, spatial interpolation easily misses the representation of the overall rainfall distribution due to undersampling if the number of stations is insufficient. In this study, two algorithms based on the multivariate regression-kriging (RK) and merging spatial interpolation techniques were developed to adjust rain fields from unreliable radar estimates using gauge observations as target values for the high-elevation Chenyulan River watershed in Taiwan. The developed geostatistical models were applied to the events of five moderate to high magnitude typhoons, namely Kalmaegi, Morakot, Fungwong, Sinlaku, and Fanapi, that struck Taiwan in the past 12 years, such that the QPESUMS’ (quantitative precipitation estimation and segregation using multiple sensors) radar rainfall data could be reasonably corrected with accuracy, especially when the sampling conditions were inadequate.