Regionalization for prediction in ungauged basins at hourly resolution is important for water resources management (e.g. floods and hydropeaking). In this paper, calibration of 26 catchments (39-3090 km 2 ) in mid-Norway was performed using hourly records and three spatially distributed (1x1 km 2 ) precipitation-runoff models: a first-order nonlinear system model (hereafter Kirchmod), the HBV model and the Basic-Grid-Model (BGM). Four regionalization methods for each model namely parameter set yielding maximum regional weighted average (MRWA) performance measures (PM), regional median of optimal parameters (RMedP), nearest neighbor (NN) and physical similarity (PS) were evaluated and compared with three benchmarks. Parameter transfer from best regional donor (BRD) and from an ideal best arbitrary single-donor (BASD), and local calibration (LC) were as benchmarks. The physical similarity attributes include hypsometric curves (PSH), land use (PSL), drainage density (PSD), catchment area (PSA), terrain slope (PSS), bedrock geology (PSR), soil type (PSSO) and combination of all (PSC).Comprehensive evaluation of single-and multi-donors, simple benchmarks and more advanced regionalization methods using multi-models, two PM and their statistical evaluation indicate that the identification of regionalization methods is dependent on the models, the PM and their statistical evaluation. In general, the PSH, PSC and BRD methods performed better for the Nash-Sutcliffe efficiency (NSE) based on boxplots and regional median values of both the NSE and relative deterioration or improvement of the NSE from the local calibration due to the regionalization. The methods also performed better for the individual catchments. The PSS, RMedP, MRWA and BRD methods performed better for the log-transformed streamflow 2 (NSEln) based on the same evaluation criteria. Similar performance to the more advanced regionalization methods of transfer of homogeneous parameter sets across the whole region from the BRD for both NSE and NSEln indicate the potential of the simple regionalization approach for predicting runoff response in the region.

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The evaluation of a recession based 'top-down' model for distributed hourly runoff simulation in macroscale mountainous catchments is rare in literature. We evaluated the Despite small number of calibrated parameters, the model is susceptible to parameter uncertainty and identifiability problems.

The implementation of weather radars in Norway by the Norwegian Meteorological Institute (met.no) has made radar a potential tool to improve hydrologic predictions through the use of distributed precipitation input. Met.no supplies gauge-adjusted quantitative hourly radar precipitation estimates. A key concern regarding the use of radar precipitation estimates in hydrology is their accuracy. In this study, the precipitation estimates from the Rissa radar in Norway were evaluated through a comparison with observations from 112 gauges used in the adjustment (dependent) and 15 gauges not included in the adjustment (independent). The comparison with daily measurements from the dependent gauges showed a decline in the radar's detection probability beyond a range of about 140 km, with a more severe decline in winter. The deviations between radar- and gauge-conditional mean precipitation were significantly higher in summer than in winter. There was an overestimation at most of the gauge locations during summer, while there were more underestimations during winter. A dependence of accuracy on range was identified from the spatial distribution of the Efficiency Index and mean absolute difference. The evaluation against the independent gauges revealed trends mostly similar to the ones obtained from comparison with the dependent gauges. The radar estimates exhibited better agreement with gauge measurements during winter. The main reasons for the errors remaining in the gauge-adjusted precipitation estimates are the absence of correction for the vertical profile of reflectivity, the use of average monthly adjustment factors, derivation of these factors using data from previous years and the use of a single reflectivity–precipitation rate (Z–R) relation.