Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

Abstract

Abstract:The performance of Satellite Rainfall Estimate (SRE) products applied to flood inundation modelling was tested for the Mundeni Aru River Basin in eastern Sri Lanka. Three SREs (PERSIANN, TRMM, and GSMaP) were tested, with the Rainfall-Runoff-Inundation (RRI) model used as the flood inundation model. All the SREs were found to be suitable for applying to the RRI model. The simulations created by applying the SREs were generally accurate, although there were some discrepancies in discharge due to differing precipitation volumes. The volumes of precipitation of the SREs tended to be smaller than those of the gauged data, but using a scale factor to correct this improved the simulations. In particular, the SRE, i.e., the GSMaP yielding the best simulation that correlated most closely with the flood inundation extent from the satellite data, was considered the most appropriate to apply to the model calculation. The application procedures and suggestions shown in this study could help authorities to make better-informed decisions when giving early flood warnings and making rapid flood forecasts, especially in areas where in-situ observations are limited.

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

“…With regard to GPDs, as has been reported by other authors [56,57], the volumes of precipitation of the satellite precipitation products tended to be smaller than those of the gauged data in most cases, and these differences are greater the drier the watershed is, as in the cases of GAR and RVA. MSWEP and PERSIANN show the highest differences in accumulated precipitation, normally lower, except for the semi-arid watershed (RVA).…”

Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar

“…(1) IMERG-F is generally superior to 3B42V7 in hydrological simulations in many regions owing to its enhanced precipitation estimates; (2) the IMERG post-real-time product (IMERG-F) tends to have better hydrological abilities than the near-real time products (IMERG-E and IMERG-L); and (3) although a few previous studies show that the earlier versions of GSMaP are feasible for flash flood simulations [10,51] and flood inundation modeling [9,55], the hydrological evaluations of the latest GPM-era versions of the GSMaP products were seldom reported in recent studies. Although Section 4.2 only demonstrates the hydrological performance of the best near-real-time SPP (3B42RT) and the comparatively better post-real-time SPPs (IMERG-F and 3B42V7), our study also evaluated the hydrological utility of the other five SPPs (IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE), which is summarized in Table S2.…”

“…The representative SPPs in these two eras include Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) [3], Climate Precipitation Center morphing method (CMORPH) [4], Climate Hazards Group Infrared Precipitation with Stations [5], TRMM Multi-satellite Precipitation Analysis (TMPA) [1], Global Satellite Mapping of Precipitation (GSMaP) [6], and Integrated Multi-satellite Retrievals for GPM (IMERG) [2]. These SPPs generally provide quasi-global precipitation maps on high spatiotemporal resolutions (finer than 0.25° spatial resolution and shorter than the daily time interval); TRMM-era SPPs, in particular, have been widely adopted in hydrological applications in many parts of the world [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. techniques.…”

“…GSMaP provides a series of precipitation estimates with different latencies, such as near-real-time product (GSMaP-NRT), post-real-time product with MW-IR reanalysis (GSMaP-MVK), and gauge-corrected post-real-time product (GSMaP-GAUGE). These products have been used for hydrological applications, such as flash flood simulations in the Nile River basin in Egypt [51], the Huong River basin in Vietnam [52], the Karpuz River basin in Turkey [10], and the Jhelum, Chenab, and upper Indus River basins in Pakistan [53,54]; flood inundation modeling in the Thua Thien Hue Province in Vietnam [55] and the Mundeni Aru River basin in Sri Lanka [9]; daily streamflow simulations in the Tocantins-Araguaia basin in Brazil [7], the Soyang dam basin in South Korea [56], and the Biliu basin in China [8]; annual runoff analysis in West Africa [57]; and global surface runoff mapping [58]. However, most of these studies [7][8][9][10][51][52][53][54][55][56][57] mainly adopted TRMM-era GSMaP SPPs (e.g., GSMaP versions 04 and 05) for hydrological evaluations or applications, and the hydrological assessment of GPM-era GSMaP SPPs (e.g., versions 06 and 07) was rarely reported.…”

Variability of Microwave Scattering in a Stochastic Ensemble of Measured Rain Drops

“…The retrieval of precipitation with radars and radiometers is important for a variety of environmental applications and human activities [1]. They provide accurate precipitation estimates that are crucial for monitoring extreme climate events, such as droughts [2][3][4], floods [5][6][7], and hailstorms [8,9]. Due to its global coverage and direct measurement, radars have become an essential tool to estimate precipitation, especially in complex terrains [10][11][12] and in sparsely populated areas affected by poor rain gauge coverage [13][14][15].…”