Assessment of GPM-Era Satellite Products’ (IMERG and GSMaP) Ability to Detect Precipitation Extremes over Mountainous Country Nepal

Abstract

The reliability of satellite precipitation products is important in climatic and hydro-meteorological studies, which is especially true in mountainous regions because of the lack of observations in these areas. Two recent satellite rainfall estimates (SREs) from Global Precipitation Measurement (GPM)-era—Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-V06) and gauge calibrated Global Satellite Mapping of Precipitation (GSMaP-V07) are evaluated for their spatiotemporal accuracy and ability to capture extreme precipitation events using 279 gauge stations from southern slope of central Himalaya, Nepal, between 2014 and 2019. The overall result suggests that both SREs can capture the spatiotemporal precipitation variability, although they both underestimated the observed precipitation amount. Between the two, the IMERG product shows a more consistent performance with a higher correlation coefficient (0.52) and smaller bias (−2.49 mm/day) than the GSMaP product. It is worth mentioning that the monthly gauge-calibrated IMERG product yields better detection capability (higher probability of detection (POD) values) of daily precipitation events than the daily gauge calibrated GSMaP product; however, they both show similar performance in terms of false alarm ratio (FAR) and critical success index (CSI). Assessment based on extreme precipitation indices revealed that the IMERG product outperforms GSMaP in capturing daily precipitation extremes (RX1Day and RX5Day). In contrast, the GSMaP product tends to be more consistent in capturing the duration and threshold-based precipitation extremes (consecutive dry days (CDD), consecutive wet days (CWD), number of heavy precipitation days (R10mm), and number of extreme precipitation days (R25mm)). Therefore, it is suggested that the IMERG product can be a good alternative for monitoring daily extremes; meanwhile, GSMaP could be a better option for duration-based extremes in the mountainous region.

Projected Drought Conditions over Southern Slope of the Central Himalaya Using CMIP6 Models

“…in the north. Being located in the tropical northern limit with diversified topography, the country features a very diverse climate from tropical savanna in low-elevation to polar frost in high-the Himalayas (Karki et al 2015;Nepal et al 2021;Shrestha et al 2021). Annual precipitation is predominately governed by the summer monsoon and westerly winter precipitation (Duncan et al 2013;Ehsan et al 2017;Kansakar et al 2004).…”

Response of Soil Water Storage to Meteorological Factors in Alpine Shrub Meadow on Northeastern Qinghai–Tibetan Plateau

“…Many scholars have adopted modeling methods [7,8] to simulate the impact of climate change and predict the change on water storage in the coming decades, using remote sensing images, satellite altimetry data, and gravimetry data, such as ICESat/GLAS altimetry data, MSS/TM/ETM plus imagery [9], and GRACE Satellites [10]; though the results obtained from these methods have been limited. However, many recent studies have shown that extensive evaluation of global-scale high-resolution satellite-based rainfall (SBR) products, especially IMERG-V5 and GSMaP-V7 [11][12][13], which can be a good alternative for monitoring daily precipitation, explains the merit of precipitation products in a near-real-time way. Dekai Lu (2018) indicated that accurately measuring light rainfall and winter snow was still a challenging task for the current satellite precipitation retrievals in the paper "Evaluation and Hydrological Utility of the Latest GPM IMERG V5 and GSMaP V7 Precipitation Products over the Tibetan Plateau" [14].…”

A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data

“…For instance, Khan et al (2016) record lower IMERG performance in continental climates with dry summers compared to those with year-round precipitation. Further, performance of IMERG data has been extensively studied in several locations worldwide, where IMERG typically underestimates high intensity rainfall in complex terrain (e.g., Lu et al, 2019;Maggioni et al, 2017;Mayor et al, 2017;Nascimento et al, 2021;Navarro et al, 2020;Nepal et al, 2021;Rojas et al, 2021). Decreased IMERG performance due to orography is a known issue and remains an area for future work (Tan et al, 2019).…”