In this study, high‐resolution climate change data from the regional climate models COSMO‐CLM, HIRHAM, RegCM, and REMO were evaluated in the Greater Alpine Region (GAR; 4°W–19°W and 43°N–49°N) and three additional subareas of 1.5° by 1° in size. Evaluation statistics include mean temperature and precipitation, frequency of days with precipitation over 1 mm and over 15 mm, 90% quantile of the frequency distribution, and maximum number of consecutive dry days. The evaluation for the 1961–1990 period indicates that the models reproduce spatial precipitation patterns and the annual cycle. The mean precipitation domain bias varies between 11% and 40% in winter season and between −14.5% and 11% in summer. Larger errors are found for other statistics and in the various regions. No single best model could be identified comparing modeled precipitation characteristics with observational reference. The study shows that there is still high uncertainty in the expected climate change. Furthermore, future temperature and precipitation changes simulated with different SRES scenarios and calculated by different RCMs overlap. The temperature calculations for the period 2071–2100 related to the period 1961–1990 in the GAR area show an increase in the monthly mean 2m temperature of up to 4.8 K in summer. In the GAR area, a precipitation decrease of up to 29% in summer and precipitation increase of approximately 20% in the winter season is simulated. Summer and autumn temperatures are expected to increase more than winter and spring temperatures. Detailed analysis reveals that the different regional climate model runs based on different regional models, different driving global models and different emission scenarios show similar trends, but differ in the magnitude of the expected climate change signal. All models seem to agree on the increased frequency of high‐precipitation events in the winter season.

Three methods for estimating instantaneous sensible heat flux (H) over Savannah environment in West Africa were compared: first, satellite derived estimations using the Surface Energy Balance Algorithm for Land (SEBAL) method [Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998a. A remote sensing energy balance algorithm for land, SEBAL: 1. Formulation. J. Hydrol. 212-213,198-2121; secondly, measurements at two test sites in Ghana with a large-aperture scintillometer (LAS); third, high resolution mesoscale meteorological simulations using the MM5 (5th-Generation Penn State/NCAR) mesoscale modelling system. Satellite-derived sensible heat flux was based on seven NOAA-16 AVHRR images that were processed for a 2-week period in December 2001 (dry season) and were compared to LAS-data and MM5 simulation results.A methodology based on Gaussian Error Propagation is presented to derive uncertainties in satellite derived sensible heat flux due to (a) input data, (b) coefficients to determine leaf area index (LAI) and (c) methodological differences in estimating surface temperature To. Total computed relative uncertainty in H was 15% for the Tamale test site and 20% for the Ejura. site. Uncertainties in instantaneous evapotranspiration lambda(E), however, are much smaller than uncertainties of H. This results due to the same bias in H and R-n - G. For LAS-data, an uncertainty analysis due to input data was performed which showed relative uncertainty of 8%for the Tamale site and 7% for Ejura. Satellite derived net radiation (R-n) was underestimated in comparison to ground measurements which finally caused an underestimation of H. Satellite estimates of H using spatially interpolated ground based measurements of net radiation showed good agreement to LAS data.MM5-computed latent heat flux showed very low values for the entire region. This caused a serious relative MM5-overestimation of sensible heat flux in comparison to LAS and satellite derived estimates.It could be shown that Gaussian Error Propagation can serve as an essential tool to asses the reliability of satellite derived sensible heat fluxes. The resulting uncertainties give information on sensitivities in estimating H and therefore provide a tool for validation purposes. (c) 2007 Elsevier B.V. All rights reserved.

The quality of hydrological modelling is limited due to the restricted availability of high resolution temporal and spatial input data such as temperature, global radiation, and precipitation. Radar-based rain measurements provide good spatial information. On the other hand, using radar data is accompanied by basic difficulties such as clutter, shielding, variations of Z/R-relationships, beam-resolution and attenuation. Instead of accounting for all errors involved separately, a robust Z/R-relationship is estimated in this study for the short range (up to 40 km distance) using inverse hydrological modelling for a continuous period of three months in summer 2001. River gauge measurements from catchment sizes around 100 km2 are used to estimate areal precipitation and finally Z/R-relationships using a calibrated hydrological model. The study is performed in the alpine Ammer catchment with very short reaction times of the river gauges to rainfall events.