Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Precipitation measurements exhibit large cold-season 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. <br><br> 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. <br><br> 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. <br><br> The 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 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

Flood modelling: Parameterisation and inflow uncertainty

This paper presents an analysis of uncertainty in hydraulic modelling of floods, focusing on the inaccuracy caused by inflow errors and parameter uncertainty. In particular, the study develops a method to propagate the uncertainty induced by, firstly, application of a stage–discharge rating curve and, secondly, parameterisation of a onedimensional hydraulic model by way of the power function and the conditioning of Manning’s roughness coefficients. The proposed methodology was applied to a 98 km reach of the River Po, Italy. Model performance was evaluated using two independent sets of observed water levels in the river reach within a generalised likelihood uncertainty estimation framework. The inflow uncertainty was found to have a greater contribution to the overall uncertainty of the 1D model than the roughness parameters. Independent parameter conditioning and validation, as well as the uncertainty analysis, showed satisfactory model performance. When conditioned on one flood event, the model adequately simulated flood levels and high water marks for another (independent) event, as the observations were within 90% confidence interval of the simulation ensemble.Water ManagementCivil Engineering and Geoscience