Climatology and Interannual Variability of Floods during the TRMM Era (1998–2013)

Abstract

Spatial and temporal variations of global floods during the TRMM period (1998–2013) are explored by means of the outputs of the Dominant River Routing Integrated with VIC Environment model (DRIVE) driven by the precipitation rates from the TRMM Multisatellite Precipitation Analysis (TMPA). Climatological and seasonal mean features of floods including frequency (FF), duration (FD), and mean and total intensity (FI and FTI) are examined and further compared to those for a variety of precipitation indices derived from the daily TMPA rain rates. In general, floods and precipitation manifest similar spatial distributions, confirming that more precipitation (both amount and frequency) often indicates higher probability of floods. However, different flood indices can be associated with different precipitation characteristics with a highly region-dependent distribution. FF and FD tend to be more related to daily precipitation frequency globally, especially the mid- to high-end precipitation frequencies (F10, F25, F50). However, FI and FTI tend to be more associated with the mean volume/magnitude of those (extreme) daily precipitation events (Pr10 and Pr25). Nonetheless, daily precipitation intensity except the very high end one (R50) generally has a relatively weak effect on floods. The precipitation–flood relations at the 10 large regions are further examined, providing an improved understanding of precipitation-related flood-generating mechanisms in different locations. On the interannual time scale, El Niño–Southern Oscillation (ENSO) can significantly affect floods in many flood-prone zones. However, it is noted that even though the ENSO effect on floods is mostly through modulating various aspects of precipitation events, significant ENSO signals in precipitation cannot always translate to an effective, simultaneous impact on floods.

Exploring the ENSO Impact on Basin‐Scale Floods Using Hydrological Simulations and TRMM Precipitation

“…The DRIVE retrospective simulations were performed using the TRMM/TMPA 3‐hourly precipitation rates for the TRMM period (1998–2013) (Yan et al, 2020). The hydrological outputs include routed runoff and discharges with spatial resolution of 0.125° × 0.125° and time resolution of 3 hr.…”

Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States

“…Other atmospheric forcing data (i.e., air temperature and wind speed) were obtained from the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (Rienecker et al 2011). More detailed descriptions of the DRIVE model, forcing inputs, and model parameter setup are presented in previous studies (Huang et al 2021;Wu et al 2014;Yan et al 2020). All forcing inputs are preprocessed at the same resolution in space and time that are consistent with model configuration.…”

“…Consequently, satellite-based precipitation products have been increasingly developed to facilitate largescale hydrological applications (Wu et al 2012a;Wu et al 2014). However, despite a wide range of hydrological studies applying satellite-based rainfall estimates for many years (Adler et al 2000;Buarque et al 2011;He et al 2017;Huffman et al 2001;Massari 2018;Meng et al 2014;Nikolopoulos et al 2013;Prakash et al 2016;Anagnostou 2012;Su et al 2011;Wu et al 2014;Yan et al 2020;Zhong et al 2019), the practical applications remain limited due to a number of error sources and uncertainties Anagnostou 2004;Sarachi et al 2015). Reliable estimation of precipitation in space and time is highly desired for hydrological applications, as uncertainties in rainfall estimates can potentially lead to large errors in simulation outputs (Arnaud et al 2002;Borga 2002;Courty et al 2018;Morin et al 2006;Rico-Ramirez 2021;Rico-Ramirez et al 2015;Smith et al 2004;Tscheikner-Gratl et al 2018;Wu et al 2017;Younger et al 2009;Zhang et al 2018).…”

From China’s Heavy Precipitation in 2020 to a “Glocal” Hydrometeorological Solution for Flood Risk Prediction

“…We urge the international hydrometeorological community to do more for better preparedness and for a better response to such catastrophic flooding events, in particular from the perspective of hydrometeorological modeling, given the projections of more frequent and extreme precipitation events under a continuously warming climate (e.g., Allan and Soden, 2008;Trenberth, 2011;Chen et al, 2020). Given the complex relationship between precipitation and flooding (Wu et al, 2017;Yan et al, 2020), detailed and accurate monitoring and better forecasting of flooding ought to be done jointly between the meteorological and the hydrological communities, through sharing observations, measurements and modeled data, modeling techniques and outputs, as well as expertise and lessons learned.…”