The accurate representation of subgrid scale effects in climate models has been an issue of great controversy. In attempt to resolve this controversy, a model-independent technique was developed in this study to upscale land surface parameters through inverse-SVAT modeling. Upscaling laws are derived, that map the distributed land surface parameters of a heterogeneous land surface to their corresponding effective parameter. Simpler averaging methods of comparable performance are derived from the inverse modeling results, which drastically reduce the computational effort. The proposed method shows better performance than most well known methods. More importantly, the method is applicable in many fields.

Water Resources and Development explores water management strategies through scientific, social and political perspectives, and uses case studies to exemplify four key development challenges: economic growth, poverty reduction, competition and conflict over water, and adaptation to climate change

Coffea arabica, one of the economically most important crops worldwide, occurs naturally in the undergrowth of montane rainforests of Ethiopia. The study provides the first detailed ecophysiological investigations of wild coffee populations. It demonstrates the inter- and intra-regional variability in phenotypic and hydrological characteristics of wild coffee. The results reveal very different strategies of wild coffee seedlings for coping with drought stress. The ecophysiological diversity shows the importance of Ethiopian wild coffee populations as gene pools for future breeding programs, and underlines the need for an in-situ conservation strategy. The study includes recommendations for coffee forest management and the use of wild arabica coffee in Ethiopia.

Soils of irrigated lands in the Aral Sea Basin are often plagued by high salinity,hampering profitable agriculture on these soils. In this context, the present study has three specific objectives: to identify techniques that enable a rapid estimation of soil salinity, to characterize its spatial distribution and to estimate its spatial distribution based on readily obtainable environmental parametersusing a Neural Network Model Approach. Topsoil salinity was highly variable atshort distances and terrain attributes were the most influential factors. The use of relationships between environmental attributes and soil salinity for upscaling spatial distribution of soil salinity from farm to district level proved to be satisfactory. A possible application is the development of salinity prediction toolsfor farm-level decision making.