The calibration objective of Niger-HYPE was not to provide an optimal model for a specific region, but rather to identify a model with the best performance averaged across the model domain (i.e. at all evaluation stations simultaneously). The Niger-HYPE model parameters were therefore regionalized based on catchment characteristics (land cover, soil type, area, and tributary). The parameters were simultaneously calibrated against 56 daily river discharge stations (ABN, 2008; GRDC, 2012) as well as monthly satellite evapotranspiration estimates covering the basin at 1 km2 resolution (Mu et al., 2011). The calibration period was 1994-2009, and the evaluation period 1979 to 1993. However in order to be consistent with the other results on HypeWeb, the performance shown here represents the period 1980-2009.
Different performance criteria are used here to define the performance of the model towards the observed discharge. The criteria presented here investigate the relative adequacy of the model with regard to timing, variability and volume error. Here, we present model performance at different stations in terms of the Nash-Sutcliffe Efficiency (NSE; Nash and Sutcliffe, 1970), the correlation coefficient, the relative error in mean, the relative error in standard deviation, and Kling-Gupta Efficiency (KGE; Gupta et al., 2009). The optimum value for each criterion (describing a perfect model) is not the same: NSE, KGE and correlation coefficient have optimum value at 1, while the remaining relative-error-based criteria have optimum value at 0.
The colour of the circles correspond to a range, for which the segments are presented at the histogram plot. The histogram depicts the number of stations that have performance within each segment.
The overall model performance in terms of mean annual discharge is presented in the “Simulated versus observed river discharge” plot. A perfect agreement between simulated and observed mean annual discharge would correspond to dots lying on the red 1:1 line. The user can click on a specific dot in the graph, and the selected station will appear in the map. The user can further select one of the “evaluation criteria” and the histogram is automatically updated for the selected criterion. Additional information for individual stations can be presented by Selection of individual stations (simply by left-clicking a circle) would provide additional information about the station (name and upstream area) and basic flow characteristics, i.e. simulated and observed mean discharge.
ABN, 2008. Niger-HYCOS Hydrological Information System. URL: http://nigerhycos.abn.ne/ (accessed 3.1.12)
GRDC, 2012. The GRDC world-wide repository of river discharge data and associated metadata. Glob. Runoff Data Cent. Fed. Inst. Hydrol. BfG Kobl. Ger. URL: http://www.bafg.de/GRDC/ (accessed 2.1.12).
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80–91. doi:10.1016/j.jhydrol.2009.08.003
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Journal of Hydrology, 10, 282–290.
Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800. doi:10.1016/j.rse.2011.02.019