Performance evaluation of CFSR, MERRA-2 and TRMM3B42 data sets in simulating river discharge of data-scarce tropical catchments: a case study of Manafwa, Uganda

Abstract

Data scarcity has been a huge problem in modelling various catchments especially in the tropical region. Satellite data and different statistical methods are being used to improve the quality of conventional meteorological data. However, the potential of using these data needs to be further investigated. This paper evaluates the performance of three reanalysis datasets in hydrological modelling of the Manafwa Catchment, Uganda. Two reanalysis datasets were selected for studying both rainfall and temperature in Manafwa, whereas a satellite algorithm was selected for studying rainfall alone. The Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), which has a horizontal resolution of 50 km and is suitable for investigating rainfall in small precipitation systems, and the Climate Forecast System Reanalysis and Reforecast (CFSR), which has a horizontal resolution of 38 km, were chosen as the reanalysis datasets. The Tropical Rainfall Measuring Mission (TRMM3B42) data were used as the satellite product in this study. The SWAT was set up to compare the performance of reanalysis datasets with that of the observed data. The model performance indicators indicated that, at daily time steps, all the three datasets produced the values of Nash–Sutcliff Efficiency (NSE≥0.4), coefficient of determination (R2≥0.4) and Percent Bias ±25%. Despite a general underperformance compared to MERRA-2, CFSR performed better than TRMM. On applying generated bias corrections for precipitation and temperature climate data, overall results showed that the bias-corrected data outperformed the original data. We conclude that, in the absence of gauged hydro-meteorological data, bias-corrected MERRA-2, CFSR and TRMM data could be used for simulating river discharge in data-scarce areas such as the tropical catchments in Uganda.

Global PM2.5 Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios

“…To train the deep learning model, monthly average meteorological data that were taken from the MERRA-2 dataset include surface temperature, wind speed, specific humidity, planetary boundary layer height (PBLH), and sea level pressure, as these parameters can strongly influence the PM 2.5 concentration . Several studies have compared the MERRA-2 dataset with ground-based observations and other reanalysis datasets, and results showed that the MERRA-2 data are able to represent the surface meteorological conditions. ,− The performance of MERRA-2 on temperature, relative humidity, wind speed, PBLH, and sea level pressure is summarized in Table S1. For example, when compared with the ground observation data from China, the RMSE, mean bias (MB), and R values for temperature were 3.62 K, −2.14 K, and 0.95, respectively .…”