Abstract. To ensure reliable model understanding of water movement and distribution in terrestrial systems, sufficient and good quality hydro-meteorological data are required. Limited availability of ground measurements in the vast majority of river basins world-wide increase the value of alternative data sources such as satellite observations in modelling. In the absence of directly observed river discharge data, other variables such as remotely sensed river water level may provide valuable information for the calibration and evaluation of hydrological models. This study investigates the potential of the use of remotely sensed river water level, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River Basin in Zambia. A distributed process-based rainfall runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. Following a step-wise approach, various parameter identification strategies were tested to evaluate the potential of satellite altimetry data for model calibration. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. In a next step, three alternative ways of further restricting feasible model parameter sets based on satellite altimetry time-series from 18 different locations, i.e. virtual stations, along the Luangwa River and its tributaries were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash-Sutcliffe efficiency of ENS,Q = 0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95 = 0.61 – 0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q = −1.4 (ENS,Q,5/95 = −2.3 – 0.38) with respect to discharge was obtained. Depending on the parameter selection strategy, it could be shown that altimetry data can contain sufficient information to efficiently further constrain the feasible parameter space. The direct use of altimetry based river levels frequently over-estimated the flows and poorly identified feasible parameter sets due to the non-linear relationship between river water level and river discharge (ENS,Q,5/95 = −2.9 – 0.10); therefore, this strategy was of limited use to identify feasible model parameter sets. Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an over-estimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95 = −2.6 – 0.25). However, accounting for river geometry proved to be highly effective; this included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth, and applying the Strickler-Manning equation with effective roughness as free calibration parameter to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well with an optimum of ENS,Q = 0.60 (ENS,Q,5/95 = −0.31 – 0.50). It was further shown that more accurate river cross-section data improved the water level simulations, modelled rating curve and discharge simulations during intermediate and low flows at the basin outlet at which detailed on-site cross-section information was available. For this case, the Nash-Sutcliffe efficiency with respect to river water levels increased from ENS,SM,GE = −1.8 (ENS,SM,GE,5/95 = −6.8 – −3.1) using river geometry information extracted from Google Earth to ENS,SM,ADCP = 0.79 (ENS,SM,ADCP,5/95 = 0.6 – 0.74) using river geometry information obtained from a detailed survey in the field. It could also be shown that increasing the number of virtual stations used for parameter selection in the calibration period can considerably improve the model performance in spatial split sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly or ungauged basins.
