Water is essential for all life on earth. Global population increase and climate change are projected to increase the water stress, which already today is very high in many areas of the world. The differences between the largest and smallest global runoff estimates exceed the highest continental runoff estimates. These differences, which are caused by different modelling and measurement techniques together with large natural variabilities need to be further addressed. This thesis focuses on global water balance models that calculate global runoff, evaporation and water storage from precipitation and other climate data.
A new global water balance model, WASMOD-M was developed. Already when tuned against the volume error it reasonable produced within-year runoff patterns, but the volume error was not enough to confine the model parameter space. The parameter space and the simulated hydrograph could be better confined with, e.g., the Nash criterion. Calibration against snow-cover data confined the snow parameters better, although some equifinality still persisted. Thus, even the simple WASMOD-M showed signs of being overparameterised.
A simple regionalisation procedure that only utilised proximity contributed to calculate a global runoff estimate in line with earlier estimations. The need for better specifications of global runoff estimates was highlighted.
Global modellers depend on global data-sets that can have low quality in many areas. Major sources of uncertainty are precipitation and river regulation. A new routing method that utilises high-resolution flow network information in low-resolution calculations was developed and shown to perform well over all spatial scales, while the standard linear reservoir routing decreased in performance with decreasing resolution. This algorithm, called aggregated time-delay-histogram routing, is intended for inclusion in WASMOD-M.