The calibration objective in the large scale applications is not to provide an optimal model for a specific region, but rather to identify a model that achieves best possible performance averaged across many gauged basins at all scales.
Global parameter values (for e.g. precipitation, landuse, evaporation, river routing, lake dynamics, floodplains) were estimated using a step-wise multi-basin approach for groups of parameters and aimed at finding robust values also valid for ungauged basins. Catchments were selected globally to represent each process calibrated and the estimated parameters were then applied wherever relevant, world-wide.
Before the step-wise procedure, the global PET parameter values were fixed using the MODIS global evapotranspiration product and then time-series from 5338 gauges of river flow across the globe (with >10 years of observations) were used for model calibration and evaluation (half for calibration and half for independent validation). The calibration period was 1981-2012 proceeded by 15 years of initialization and different metrics were used depending on the character of the parameter (e.g. volume error for precipitation and PET parameters, correlation coefficient when timing was of importance, Kling-Gupta Efficiency (KGE) if both water volume and timing was required.
This first attempt to calibrate World-wide HYPE resulted in an average monthly KGE=0.4.
The World-wide HYPE (WWH) model shows large variation in model performance, spatially and between various flow signatures. It should be used with caution especially in dry regions. However, there are good potential for model improvements and the calibration cycle will be repeated. For the next version, there will be special focus on water balance, soil storage and hydrological features such as lakes, reservoirs, glaciers and floodplains.
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. In 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 circle corresponds to a range of model performance, which segments are presented at the histogram plot; depicting 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. Selecting the “view time-series chart”, the observed (black dots) and modelled (red line) discharge series are presented allowing visual model evaluation.
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Journal of Hydrology, 10, 282–290.
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