Abstract
AbstractExtreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN-Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC Morphing Technique (CMORPH). Five indices of intense rainfall were considered for the period 2000-2019 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country. While random errors are higher over the wetter and higher-elevation north- and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR and IMERG perform similar capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERG and CMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20 mm/day; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential use and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.
Highlights
Both globally and in South Asia, Bangladesh is recognized as one of the most vulnerable countries to climate change and natural disasters
Regarding the random error represented by R99pTOT in terms of correlation and error (RMSE) (Figs. 9e–g), it is clearly observed that IMERG provides the best results, with an average RMSE of 5 days at the country level, followed by CHIRPS, Climate Prediction Center (CPC) morphing technique (CMORPH), and PERSIANN, which present a similar performance (RMSE 5 10 days)
While CMORPH shows an underestimation of the number of very rainy days R20mm, IMERG presents a Bias that varies from negative to positive from south to north, ranging from 26 to 8 days
Summary
Both globally and in South Asia, Bangladesh is recognized as one of the most vulnerable countries to climate change and natural disasters. Extreme hydrometeorological events can lead to floods, prolonged waterlogging, and landslides (Bhowmik et al 2021). These disproportionately affect rural livelihoods and can damage infrastructure (Eckstein et al 2020). These events occur predominantly during the monsoon season, typically from June to September when rainfall is concentrated (Ahmed and Karmakar 1993). The impact of intense rainstorms can be enhanced by Bangladesh’s geographical characteristics including proximity of the sea, and generally low-elevation and flat terrain (Mirza 2011). Combined with one of the world’s highest population densities and given that nearly 60% of Bangladesh’s land is Denotes content that is immediately available upon publication as open access
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