A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901-2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product's ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of -2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901-2018 was in the range of -4.5 to 4.3%, NRMSE between 9 and 19% and R in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.
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http://dx.doi.org/10.1038/s41597-022-01568-z | DOI Listing |
Environ Sci Pollut Res Int
December 2024
Department of Civil and Environmental Engineering, University of Perugia, Via Duranti 93, 06125, Perugia, Italy.
This study aims to analyze the performances and correlation of the standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) from the perspective of supplying effective indicators for drought risk management prevention. Indices have been evaluated using long time series of precipitation and temperature data (from 1961 to 2020) gauged and validated in the land monitoring system of the Umbria region (central Italy). Results show how SPEI can evaluate better the drought phenomena, both in terms of occurred events and in terms of trends.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Civil, Environmental, and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL, 33431, USA.
Clim Dyn
October 2024
U.S. National Science Foundation National Center for Atmospheric Research, 1850 Table Mesa Dr., Boulder, CO 80305 USA.
Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations.
View Article and Find Full Text PDFEnviron Monit Assess
October 2024
Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia.
The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP.
View Article and Find Full Text PDFPLoS One
September 2024
School of Water, Energy and Environment, Cranfield University, Bedford, United Kingdom.
Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures.
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