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Remote Sensing of Terrestrial Water Dynamics with Its Implications in Water Resources Management

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Dear Colleagues,

Over the last couple of decades, remotely sensed datasets obtained from various types of instruments has been successfully used to help us better understand relatively larger scale terrestrial water dynamics in different water column layers with unprecedented spatio-temporal coverage and accuracy. Those multi-dimensional observations include (1) surface water elevation changes from vertical profiling instruments such as satellite radar/laser altimetry, (2) planar inundation extents from multispectral and synthetic aperture radar (SAR) images, (3) inhomogeneous surface water elevation changes from interferometric SAR, (4) total and ground water storage changes from GRACE and GRACE follow-on missions, (5) soil moisture changes from passive microwave sensors, and so on. However, each sensor has its own pros and cons depending on the characteristics of the instruments and their different spatial and temporal resolutions.

This Special Issue solicits scientific research and review articles advancing our knowledge of terrestrial water dynamics, including extreme events by innovative use of remotely sensed observations from one or more different types of sensors. Articles presenting innovative applications of remote sensing data toward more efficient management of water resources with a holistic view of the dynamic state available from space are also encouraged.

Dr. Hyongki Lee
Dr. Kuo-Hsin Tseng
Dr. Duong Du Bui
Dr. Farrukh Chishtie
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25 pages, 4068 KiB

Open AccessArticle

Geostatistical Based Models for the Spatial Adjustment of Radar Rainfall Data in Typhoon Events at a High-Elevation River Watershed

by Keh-Han Wang, Ted Chu, Ming-Der Yang and Ming-Cheng Chen

Remote Sens. 2020, 12(9), 1427; https://doi.org/10.3390/rs12091427 - 01 May 2020

Cited by 5 | Viewed by 2327

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. The interpolated rainfall values by the RK and merging techniques were cross validated with the gauge measurements and compared to the interpolated results from the ordinary kriging (OK) method. The comparisons and performance evaluations were carried out and analyzed from three different aspects (error analysis, hyetographs, and data scattering plots along the 45-degree reference line). Based on the results, it was clearly shown that both of the RK and merging methods could effectively produce reliable rainfall data covering the study watershed. Both approaches could improve the event rainfall values, with the root-mean-square error (RMSE) reduced by up to roughly 30% to 40% at locations inside the watershed. The averaged coefficient of efficiency (CE) from the adjusted rainfall data could also be improved to the level of 0.84 or above. It was concluded that the original QPESUMS rainfall data through the process of RK or merging spatial interpolations could be corrected with better accuracy for most stations tested. According to the error analysis, relatively, the RK procedure, when applied to the five typhoon events, consistently made better adjustments on the original radar rainfall data than the merging method did for fitting to the gauge data. In addition, the RK and merging methods were demonstrated to outperform the univariate OK method for correcting the radar data, especially for the locations with the issues of having inadequate numbers of gauge stations around them or distant from each other. Full article

(This article belongs to the Special Issue Remote Sensing of Terrestrial Water Dynamics with Its Implications in Water Resources Management)

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