Application of satellite-derived rainfall for hydrological modelling in the data-scarce Black Volta trans-boundary basin

Abstract

This study, conducted in the Black Volta basin of Ghana, determined how well TRMM Multi-Satellite Precipitation Analysis (TMPA) data compare with rain gauge measurements. The potential of using the TMPA data as inputs into a hydrological model for runoff simulation in a data-scarce basin was also assessed. Using a point-to-grid approach, accumulations of ground measured rainfall on daily, monthly and annual time scales were compared with accumulations derived from TMPA daily rainfall grids. The TMPA derived data together with other free global data were used as input into the soil and water assessment tool (SWAT) to set up a hydrological model for the basin. This model was calibrated and validated using streamflow data from a station located downstream of the basin. The study results showed a correlation from 0.85 to 0.92 for the monthly accumulated rainfall. Also, good Nash–Sutcliffe efficiencies of 0.94 and 0.67 were obtained for calibration and validation, respectively, on monthly scale. Moreover, simulation of streamflow was ‘satisfactory’ to ‘very good’ in terms of trends and residual variation. The study, therefore, shows that the use of satellite rainfall in the basin would be of great benefit considering the difficulties in accessing data across the basin.

Hydrological Modeling in Data-Scarce Catchments: The Kilombero Floodplain in Tanzania

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Evaluating the present annual water budget of a Himalayan headwater river basin using a high‐resolution atmosphere‐hydrology model

“…2005 ;Bolch et al, 2012;Hartmann and Andresky, 2013], and the limitations of coarse resolution dynamical models such as Global Circulation Models over complex terrain [Fyfe and Flato, 1999;Mass et al, 2002;Leung and Qian, 2003;Salathé et al, 2008]. A first step toward addressing these challenges has been the development of gridded precipitation estimates derived from a variety of sources such as satellitebased data, i.e., Tropical Rainfall Measuring Mission (TRMM) [Huffman et al, 2007;Yan et al, 2016;Adjei et al, 2016]; reanalysis data, i.e., ERA-Interim [Dee et al, 2011] and the WATCH Forcing Data Era-Interim [Weedon et al, 2014]; and rain gauge-based data, i.e., Climate Research Unit [Mitchell and Jones, 2005], Climate Prediction Center [Xie et al, 2010] and the Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) [Yatagai et al, 2012;Xu et al, 2016]. While the proliferation of remotely sensed, reanalysis and merged products has been a positive development for researchers, many studies find that the aforementioned data sets are often inconsistent with each other [e.g., Palazzi et al, 2013;Yatagai et al, 2012;Ménégoz et al, 2013].…”

Hydrological projections under climate change in the near future by RegCM4 in Southern Africa using a large-scale hydrological model

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