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Context Although uncertainties are ubiquitous in landscape planning, so far, no systematic understanding exists regarding how they should be assessed, appropriately communicated and what impacts they yield on decision support. With increasing interest in the role of uncertainties in science and policy, a synthesis of relevant knowledge is needed to further promote uncertainty assessment in landscape planning practice. Objectives The aim of this paper is to synthesize knowledge about types of uncertainties in landscape planning, of methods to assess these uncertainties, and of approaches for appropriately coping with them. Methods The paper is based on a qualitative literature review of relevant papers identified in the ISI Web of Knowledge and supplemented by frequently cited publications. The identification and synthesis of relevant information was guided by a developed framework concerning uncertainty in landscape planning. Results The main types of uncertainties identified in landscape planning are data-, model-, projection-and evaluation uncertainty. Various methods to address these uncertainties have been identified, including statistical methods for the assessment of uncertainties in planning approaches that help to cope with uncertainties. The integration of uncertainty assessments into landscape planning results is lacking. Conclusions The assessment of uncertainties in landscape planning have been addressed by science, but what is missing are considerations and ideas on how to use this knowledge to foster uncertainty analysis in landscape planning practice. More research is needed on how the application of identified approaches into landscape planning practice can be achieved and how these results might affect decision makers.

This paper develops a method to explore how alternative scenarios of the expansion of maize production for biogas generation affect biodiversity and ecosystem services (ES). Our approach consists of four steps: (i) defining scenario targets and implementation of assumptions; (ii) simulating crop distributions across the landscape; (iii) assessing the ES impacts; and (iv) quantifying the impacts for a comparative trade-off analysis. The case study is the region of Hannover, Germany. One scenario assumes an increase of maize production in a little regulated governance system; two others reflect an increase of biogas production with either strict or flexible environmental regulation. We consider biodiversity and three ES: biogas generation, food production and the visual landscape. Our results show that the expansion of maize production results in predominantly negative impacts for other ES. However, positive effects can also be identified, i.e., when the introduction of maize leads to higher local crop diversity and, thus, a more attractive visual landscape. The scenario of little regulation portrays more negative impacts than the other scenarios. Targeted spatial planning, implementation and appropriate governance for steering maize production into less sensitive areas is crucial for minimizing trade-offs and exploiting synergies between bioenergy and other ES.

A key challenge of environmental planning is to craft recommendations for future sustainable spatial development amid ubiquitous uncertainties. This paper aims to explore how different data uncertainties, usually unknown to the planner, may influence environmental planning recommendations. We apply a case study-based approach, in which we provide three illustrative examples of how data with different kinds and levels of uncertainty affect environmental assessments and, by that, the decision-support provided by environmental planning. The cases stem from different spatial levels in Germany and consider ‘Regional soil-based climate change mitigation’ in the region of Hannover, ‘State-wide habitat conservation siting’ in the federal state of Saxony-Anhalt, and ‘National renewable energy planning’. Based on the three examples, we discuss implications for planning practice and derive recommendations for further research. The three cases studies illustrate the substantial effects of data uncertainty on environmental assessments and planning recommendations derived from those results. We identify four problem constellations of dealing with data uncertainty in environmental planning that relate to the severeness of uncertainty impacts, the responsibility of the decision-maker, and the kinds of impacts that wrong decisions may have. We close with recommendations for further research, among others to develop robust and pragmatic methods for identifying the uncertainty levels in environmental data and assessment results.

Water provision and distribution are subject to conflicts between users worldwide, with agriculture as a major driver of discords. Water sensitive ecosystems and their services are often impaired by man-made water shortage. Nevertheless, they are not sufficiently included in sustainability or risk assessments and neglected when it comes to distribution of available water resources. The herein presented contribution to the Sustainable Development Goals Clean Water and Sanitation (SDG 6) and Life on Land (SDG 15) is the Ecological Sustainability Assessment of Water distribution (ESAW-tool). The ESAW-tool introduces a watershed sustainability assessment that evaluates the sustainability of the water supply-demand ratio on basin level, where domestic water use and the water requirements of ecosystems are considered as most important water users. An ecological risk assessment estimates potential impacts of agricultural depletion of renewable water resources on (ground)water-dependent ecosystems. The ESAW-tool works in standard GIS applications and is applicable in basins worldwide with a set of broadly available input data. The ESAW-tool is tested in the Danube river basin through combination of high-resolution hydro-agroecological model data (hydrological land surface process model PROMET and groundwater model OpenGeoSys) and further freely available data (water use, biodiversity and wetlands maps). Based on the results, measures for more sustainable water management can be deduced, such as increase of rainfed agriculture near vulnerable ecosystems or change of certain crops. The tool can support decision making of authorities from local to national level as well as private enterprises who want to improve the sustainability of their supply chains.