Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Abstract

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model; thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

Computationally Efficient Multivariate Calibration and Validation of a Grid-Based Hydrologic Model in Sparsely Gauged West African River Basins

“…The estimation of available water resources using hydrologic modeling provides important information for planners and policy makers to mitigate problems arising due to water shortages. The subject of performing hydrologic predictions in sparsely gauged West African river basins has been well covered in recent years [4][5][6][7][8][9][10][11]. Due to a continuous decline in ground-based observation networks as a consequence of political unrest and financial instability [12,13], the authors explored the possibility of setting up the semi-distributed Soil and Water Assessment Tool (SWAT) model [14][15][16] for several West African river basins in a previous study [11].…”

Evaluation of Hydrologic Modelling Using Satellite Product, and MMR Rainfall in East Java, Indonesia

“…The area of the Sampean Baru, Bedadung, and Mayang watersheds are approximately 703 km 2 , 648 km 2 , and 578 km 2 , respectively, taken from the Digital Elevation Model (DEM) with a resolution of 30x30m. The number of meteorological stations and the drainage patterns of the Sampean Baru (22), Bedadung (14), and Mayang (11) watersheds is depicted in Figure 1. The density of the rain station network per km 2 from densest to sparse for the Sampean Baru, Bedadung, and Mayang watersheds overall respectively were 31.96, 46.46, and 52.6.…”

“…Spatial scarcity of rain gauge networks or none at all, especially upstream due to the difficulty of reaching them, poses a challenge to the accuracy of hydrological models Behrangi et al, 2011;Bitew & Gebremichael, 2011; Koutsouris et al, 2016). The availabhility of freely available satellite-based rainfall products with high spatial and temporal resolution can solve the challenge or even replace the measurement of rain gauges (Fujihara et al, 2014;Koutsouris et al, 2016;Thiemig et al, 2013).…”

Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

“…However, the lack of global and sufficiently dense precipitation references makes these results of satellite-based precipitation still unreliable and inadequate for operational purposes, such as flood forecasting [35]. Therefore, a regional ground validation of satellite-based precipitation datasets based on dense gauges references, especially for their hydrological performances, still requires to be conducted [36][37][38][39][40][41][42][43][44]. Although most studies revealed the potential of satellite-based precipitation datasets for hydrological simulations, they also report error sources during the hydrological modeling of satellite-based datasets.…”