Abstract. Precipitation measurements exhibit large coldseason biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibration of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions.In 2010, a comprehensive test site for precipitation measurements was established on a mountain plateau in southern Norway. Automatic precipitation gauge data are compared with data from a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which serves as the reference. A large number of other sensors are provided supporting data for relevant meteorological parameters.In this paper, data from three winters are used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics are used to evaluate and objectively choose the model that best describes the data. A continuous adjustment function and its uncertainty are derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis does not reveal any significant misspecifications for the adjustment function, but shows that the chosen model does not describe the regression noise optimally. The adjustment function is operationally usable because it is based only on data available at standard automatic weather stations.The results show a non-linear relationship between undercatch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allow, for the first time, derivation of an adjustment function based on measurements above 7 m s −1 . This extended validity of the adjustment function shows a stabilization of the wind-induced precipitation loss for higher wind speeds.

Streamflow time series are commonly derived from stage‐discharge rating curves, but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage‐discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage‐discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast, uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time‐varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

This study explores Bayesian methods for handling compound stage-discharge relationships, a problem which arises in many natural rivers. It is assumed:(1) the stage-discharge relationship in each rating curve segment is a power-law with a location parameter, or zeroplane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage-discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.

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Abstract. Precipitation measurements exhibit large cold-season biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibrations of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions. In 2010, an extensive test-site for precipitation measurements was established at a mountain plateau in Southern Norway. Precipitation data of automatic gauges were compared with a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which served as the reference. Additionally, a large number of sensors were monitoring supportive meteorological parameters. In this paper, data from three winters were used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics were used to evaluate and objectively choose the model that is describing the data best. A continuous adjustment function and its uncertainty were derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis did not reveal any significant misspecifications for the adjustment function, but showed that the chosen model uncertainty is slightly insufficient and can be further optimized. The adjustment function is operationally usable based only on data available at standard automatic weather stations. Our results show a non-linear relationship between under-catch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allowed for the first time to derive an adjustment function with a data-tested validity beyond 7 m s−1 and proved a stabilisation of the wind-induced precipitation loss for higher wind speeds.