ALBERT

Beschreibung: Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25 % and up to 60 %, respectively. Our comparison metrics provide a quantitative measure for model–data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products.

Beschreibung: Study region: The Upper Niger and Bani River basins in West Africa. Study focus: The growing demand for food, water, and energy led Mali and Guinea to develop ambitious hydropower and irrigation plans, including the construction of a new dam and the extension of irrigation schemes. These two developments will take place upstream of sensible ecosystem hotspots while the feasibility of development plans in terms of water availability and sustainability is questionable. Where agricultural development in past decades focused mainly on intensifying dry-season crops cultivation, future plans include extension in both the dry and wet seasons. New hydrological insights for the region: Today’s irrigation demand corresponds to 7% of the average annual Niger discharge and could account to one third in 2045. An extension of irrigated agriculture is possible in the wet season, while extending dry-season cropping would be largely compromised with the one major existing Sélingué dam. An additional large Fomi or Moussako dam would not completely satisfy dry-season irrigation demands in the 2045 scenario but would reduce the estimated supply gap from 36% to 14%. However, discharge peaks may decrease by 40% reducing the inundated area in the Inner Niger Delta by 21%, while average annual discharge decreases by 30%. Sustainable development should therefore consider investments in water-saving irrigation and management practices to enhance the feasibility of the envisaged irrigation plans instead of completely relying on the construction of a flow regime altering dam.