Characterizing Spatiotemporal Variations of Hourly Rainfall by Gauge and Radar in the Mountainous Three Gorges Region

Abstract

Understanding spatiotemporal rainfall patterns in mountainous areas is of great importance for prevention of natural disasters such as flash floods and landslides. There is little knowledge about rainfall variability over historically underobserved complex terrains, however, and especially about the variations of hourly rainfall. In this study, the spatiotemporal variations of hourly rainfall in the Three Gorges region (TGR) of China are investigated with gauge and newly available radar data. The spatial pattern of hourly rainfall has been examined by a number of statistics, and they all show that the rainfall variations are time-scale and location dependent. In general, the northern TGR receives more-intense and longer-duration rainfall than do other parts of the TGR, and short-duration storms could occur in most of the TGR. For temporal variations, the summer diurnal cycle shifts from a morning peak in the west to a late-afternoon peak in the east while a mixed pattern of two peaks exists in the middle. In statistical terms, empirical model-based estimation indicates that the correlation scale of hourly rainfall is about 40 km. Further investigation shows that the correlation distance varies with season, from 30 km in the warm season to 60 km in the cold season. In addition, summer rainstorms extracted from radar rainfall data are characterized by short duration (6-8 h) and highly localized patterns (5-17 and 13-36 km in the minor and major directions, respectively). Overall, this research provides quantitative information about the rainfall regime in the TGR and shows that the combination of gauge and radar data is useful for characterizing the spatiotemporal pattern of storm rainfall over complex terrain.

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

“…The correlation distances (slope) during the NEM are found to be larger than in the SWM, indicating a higher spatial correlation of rainfall during the NEM. The observation of weaker correlation during the SWM than in the NEM is consistent and analogous to earlier reports that show smaller correlation distances during summer than in winter (Baigorria et al, 2007;Dzotsi et al, 2014;Li et al, 2014). Weak correlation in summer is attributed to the large spatial variability of rainfall due to highly localized and short-lived convective systems (Krajewski et al, 2003;Dzotsi et al, 2014;Li et al, 2014).…”

“…It is clearly evident from Fig. 6 that the correlation decreases with increasing gauge distance and increases with the accumulation time, consistent with earlier studies (Krajewski et al, 2003;Villarini et al, 2008;Luini and Capsoni, 2012;Li et al, 2014). The steepest decrease in correlation is observed with 1 h integrated rain, which shows insignificant correlation (< 0.2) after ∼ 30 km.…”

“…Earlier studies have shown the usefulness of such analysis in gauge-satellite comparisons, hydrological and meteorological modeling and setting up gauge networks (Habib et al, 2001;Krajewski et al, 2003;Ciach and Krajewski, 2006;Villarini et al, 2008;Liechti et al, 2012;Luini and Capsoni, 2012;Mandapaka and Qin, 2013;Li et al, 2014;Chen et al, 2015). Spearman correlation coefficients have been computed between each pair of rain gauge locations for different rain accumulation periods.…”

“…Measurements from such networks are also useful for the validation of precipitation estimates from microwave radars and imagers (Krajewski et al, 2003;Habib et al, 2012;Tokay et al, 2014;Dzotsi et al, 2014;Chen et al, 2015). The complexity in small-scale variability of rainfall increases in hilly terrain (Zängl, 2007;Li et al, 2014). The rainfall often becomes inhomogeneous due to topographic influence and is at times highly localized, resulting in large errors in the retrieved precipitation by passive/active remote sensors due to non-uniform beam-filling of precipitation within the satellite or radar pixel (Tokay and Ozturk, 2012).…”

“…The rainfall often becomes inhomogeneous due to topographic influence and is at times highly localized, resulting in large errors in the retrieved precipitation by passive/active remote sensors due to non-uniform beam-filling of precipitation within the satellite or radar pixel (Tokay and Ozturk, 2012). In order to understand the physical processes responsible for such variability, several studies examined the dependency of rainfall spatial variability (in terms of correlation distance, d o ) on rainfall regimes (Krajewski et al, 2003), seasons, spatial and temporal aggregation of data (Krajewski et al, 2003;Villarini et al, 2008;Luini and Capsoni, 2012;Chen et al, 2015;Prat and Nelson, 2015), sample size and extreme rain events (Habib et al, 2001) and geographical features like topography (Li et al, 2014). Proper quantification of spatial correlation distance mitigates the uncertainty in the upscaling of rainfall from point-to-areal and also helps in designing rain gauge networks (Bras and RodriguezIturbe, 1993;Villarini et al, 2008).…”