Kolbjørn Engeland

Validation of a distributed hydrological model against spatial observations

In connection with climate change studies a new hydrologic field has evolved - regional hydrological modelling or hydrologic macro modelling, which implies repeated application of a model everywhere within a region using a global set of parameters. The application of a physically based distributed hydrological model ECOMAG to river basins within the NOPEX southern region with this purpose in mind is presented. The model considers the main processes of the land surface hydrological cycle: infiltration, evapotranspiration, heat and water regime of the soil, snowmelt, formation of surface, subsurface and river runoff and groundwater. The spatial integration of small and meso-scale non-homogeneity of the land surface is a central issue both for the definition of fundamental units of the model structure and for determination of representative values for model validation. ECOMAG is based on a uniform hydrological (or landscape) unit representation of the river basin, which reflects topography, soil, vegetation and land use. As a first step the model was calibrated using standard meteorological and hydrological data for 7 years from regular observation networks for three basins. An additional adjustment of the soil parameters was performed using soil moisture and groundwater level data from five small experimental basins. This step was followed by validation of the model against runoff for 14 years from six other drainage basins, and synoptic runoff and evapotranspiration measurements performed during two concentrated field efforts (CFEs) of the NOPEX project in 1994 and 1995. The results are promising and indicate directions for further research.

Assessing uncertainties in a conceptual water balance model using Bayesian methodology

Abstract Abstract The aim of this study was to estimate the uncertainties in the streamflow simulated by a rainfall–runoff model. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in model parameters and those in model structure. The uncertainties were calculated by Bayesian statistics, and the Metropolis-Hastings algorithm was used to simulate the posterior parameter distribution. The parameter uncertainty calculated by the Metropolis-Hastings algorithm was compared to maximum likelihood estimates which assume that both the parameters and model residuals are normally distributed. The study was performed using the model WASMOD on 25 basins in central Sweden. Confidence intervals in the simulated discharge due to the parameter uncertainty and the total uncertainty were calculated. The results indicate that (a) the Metropolis-Hastings algorithm and the maximum likelihood method give almost identical estimates concerning the parameter uncertainty, and (b) the uncertainties in the simulated streamflow due to the parameter uncertainty are less important than uncertainties originating from other sources for this simple model with fewer parameters.

Regional/macroscale hydrological modelling: a Scandinavian experience

Abstract Regional hydrological modelling or hydrological macro-modelling implies the repeated use of a model everywhere within a region using a global set of parameters. A majority of parameters of the macroscale hydrological model must be estimated, a priori, using existing climate, soil and vegetation data. Observations for calibration and validation of the model are only available at a subset of sites where the model is applied. For all other sites without observations the model application needs to be based on global parameters. Ecomag is a distributed, physically-based model, adapted for application to a regular grid and is used as a platform for model development at the University of Oslo (UiO). Valuable insights into hydrological processes and incitements for model development may be gained by comparing high-quality data sets and model calculations. The inter-European multidisciplinary NOPEX (NOrthern hemisphere climate Process land surface Experiment) project is one of a few prioritized full-scale land surface experiments that provides high quality data sets for a boreal environment that are utilized herein. These are complemented with data from a new experimental area in the mountains in mid-eastern Norway around Lake Aursunden. The UiO model platform facilitates the use of different parameterizations of sub-grid variability. The present work focuses on the identification of process scales for the two study areas and related process parameterization as evaluated from the available data sets. The establishment of a regional set of parameters and data requirements are two other important issues discussed.

Propagation of rating curve uncertainty in design flood estimation

Statistical flood frequency analysis is commonly performed based on a set of annual maximum discharge values which are derived from stage measurements via a stage-discharge rating curve model. Such design flood estimation techniques often ignore the uncertainty in the underlying rating curve model. Using data from eight gauging stations in Norway, we investigate the effect of curve and sample uncertainty on design flood estimation by combining results from a Bayesian multi-segment rating curve model and a Bayesian flood frequency analysis. We find that sample uncertainty is the main contributor to the design flood estimation uncertainty. However, under extrapolation of the rating curve, the uncertainty bounds for both the rating curve model and the flood frequency analysis are highly skewed and ignoring these features may underestimate the potential risk of flooding. We expect this effect to be even more pronounced in arid and semi-arid climates with a higher variability in floods. This article is protected by copyright. All rights reserved.

Large-Scale Synoptic Weather Types and Precipitation Responsible for Landslides in Southern Norway

Open image in new window The contribution of large-scale synoptic weather types to the occurrence of weather-induced landslides was investigated for southern Norway. Landslides from the period 2000–2014 were analyzed on a regional scale, using existing climatic and landslide regionalizations. The classification provides a time series of landslide classes and Kruskal-Wallis tests and chi-tests were conducted to analyze how well the classification performs for each landslide region. The synoptic classification (SynopVis Grosswetterlagen, SVG) of daily weather types was later compared with the precipitation classification. In order to predict the occurrence of landslides within a region, a logistic regression analysis was used where the independent variables were the SVG classes, mean daily rainfall and snowmelt. The results showed that in seven of the twelve landslide regions in southern Norway the SVGs have the highest predictive power in terms of landslide occurrence. In these regions, with the exception of one, the models are significantly better than a null model, and the models are good in predicting weather-induced landslide occurrence. The highest predictive probability of weather-induced landslide occurrence is given by the weather type Zonal Ridge across Central Europe (BM), which yields a 90% probability of weather-induced landslides on the west coast.