13
SPATIOTEMPORAL SCALES OF
REMOTE SENSING PRECIPITATION
YANG HONG AND YU ZHANG
13.1 BACKGROUND ON PRECIPITATION SCALE
Precipitation variability, both in space and time, plays a key role in global climate and
water cycle, regional water resources management, and local flash flood warning.
However, currently in situ precipitation measurements are limited due to discretepoint observations; such sparse data cannot well represent precipitation high spatial
and temporal variability across scales. Remote sensing techniques such as radar and
satellite have advanced rainfall measurements at relatively high resolutions to a new
era for numerous applications. In most of the studies, remote sensing precipitation
data are usually resampled into certain spatial and temporal scales for particular
application purposes, and consequently this often causes scale-related uncertainty.
Today it is well known that the error of remote sensing precipitation estimation is
nonlinearly related to the scaling (e.g., Anagnostou and Krajewski, 1998; Gourley
et al., 2010; Hong et al., 2006; Jordan et al., 2000; Seo and Krajewski, 2010; Smith
et al., 2004, 2005; Villarini et al., 2008). Hong et al. (2006) describe that the satellite
rainfall estimation error is a function of spatial and temporal scales where higher
spatial and temporal resolution is subject to larger uncertainty. On the other hand,
from the end-user perspective, precipitation is a key forcing in hydrological modeling
and natural hazard forecasting. Wood et al. (1990) pointed out that in hydrological
modeling major factors of heterogeneity leading to spatial variability in runoff are
precipitation, topography, and soil types. The latter two factors are considered as static
through time; thus, spatiotemporal variability of precipitation dominates the uncertainty of hydrological modeling at a range of scales.
253
Scale Issues in Remote Sensing, First Edition. Edited by Qihao Weng.
2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc.
SPATIOTEMPORAL SCALES OF
REMOTE SENSING PRECIPITATION
YANG HONG AND YU ZHANG
13.1 BACKGROUND ON PRECIPITATION SCALE
Precipitation variability, both in space and time, plays a key role in global climate and
water cycle, regional water resources management, and local flash flood warning.
However, currently in situ precipitation measurements are limited due to discretepoint observations; such sparse data cannot well represent precipitation high spatial
and temporal variability across scales. Remote sensing techniques such as radar and
satellite have advanced rainfall measurements at relatively high resolutions to a new
era for numerous applications. In most of the studies, remote sensing precipitation
data are usually resampled into certain spatial and temporal scales for particular
application purposes, and consequently this often causes scale-related uncertainty.
Today it is well known that the error of remote sensing precipitation estimation is
nonlinearly related to the scaling (e.g., Anagnostou and Krajewski, 1998; Gourley
et al., 2010; Hong et al., 2006; Jordan et al., 2000; Seo and Krajewski, 2010; Smith
et al., 2004, 2005; Villarini et al., 2008). Hong et al. (2006) describe that the satellite
rainfall estimation error is a function of spatial and temporal scales where higher
spatial and temporal resolution is subject to larger uncertainty. On the other hand,
from the end-user perspective, precipitation is a key forcing in hydrological modeling
and natural hazard forecasting. Wood et al. (1990) pointed out that in hydrological
modeling major factors of heterogeneity leading to spatial variability in runoff are
precipitation, topography, and soil types. The latter two factors are considered as static
through time; thus, spatiotemporal variability of precipitation dominates the uncertainty of hydrological modeling at a range of scales.
253
Scale Issues in Remote Sensing, First Edition. Edited by Qihao Weng.
2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc.