Klaus Vormoor

Temporal Disaggregation of Daily Temperature and Precipitation Grid Data for Norway

AbstractThis paper presents a simple approach for the temporal disaggregation from daily to 3-hourly observed gridded temperature and precipitation (1 × 1 km2) on the national scale. The intended use of the disaggregated 3-hourly data is to recalibrate the hydrological model currently used by the Norwegian Water Resources and Energy Directorate (NVE) for daily flood forecasting. By adapting the hydrological model to a 3-hourly temporal scale, the flood forecasting can benefit from available meteorological forecasts with finer temporal resolution and can better represent critical events of short duration and at small spatial scales. By consulting the temporal patterns of a High-Resolution Limited-Area Model (HIRLAM) hindcast series for northern Europe with an hourly temporal and a 0.1° spatial resolution, existing daily 1 × 1 km2 grids for temperature and precipitation covering all of Norway (the seNorge data) were disaggregated into 3-hourly values for the time period September 1957 to December 2010. For ...

Geostatistical regionalization of daily runoff forecasts in Norway

The national flood forecasting service in Norway applies the conceptual HBV model in 117 catchments of different scales. Daily runoff forecasts from these 117 catchments are regionalized for nationwide qualitative flood risk regionalization displayed in a map. The regionalization is performed without considering scale dependency in runoff response in different-sized catchments. We developed a simple catchment classification approach that allows the incorporation of catchment scale into qualitative runoff forecasting regionalization by geostatistical methods such as ordinary kriging (OK) and inverse distance weighting (IDW). Slightly improved regionalization results were obtained using this classification approach. We further evaluated the predictive performance of OK as this is the current operational approach for qualitative runoff forecast regionalization used in Norway. The results suggest that OK regularly predicts correct tendencies in runoff development and often with small errors. However, the results further suggest that the runoff normalization procedure (which is necessary to make runoff data comparable) provides another source of errors in flood risk regionalization. We also compare the predictive performance of OK with IDW as a less complex method and top-kriging (TK), which is an approach especially developed for interpolating river network-related data. The validation results show that TK performs best, whereas OK tends to perform only slightly better than IDW.

Hydrological model parameter (in)stability – “crash testing” the HBV model under contrasting flood seasonality conditions

ABSTRACT This paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of −5 to −17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa.

When timing matters-considering changing temporal structures in runoff response surfaces

Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21% influence), while it is negligible for mean runoff.