Using global datasets to create environmental profiles for data-poor regions: a case from the Irrawaddy and Salween River Basins.

The increasing availability of spatial data inspires the exploration of previously less-studied, yet regionally and nationally important areas, such as the Irrawaddy and Salween River Basins in Southeast Asia. This article documents our experience using global datasets to create environmental basin profiles in these two basins. Our approach draws on the concepts of freshwater vulnerability assessments that guided the selection of indicators. Data on land use, population distribution and fertilizer load were used. The unit of analysis was chosen to distinguish areas with similar bio-geographical characteristics, such as the critical delta areas. Results were further discussed for sub-areas that experience relatively the most pressure in terms of examined indicators within the studied area. The river mouths of both rivers had the most intensive land use and high population density. They are also home to important ecosystems and are sensitive to changes in upstream areas. Our study presents a concise and spatially distributed view of the environmental basin profiles of the Irrawaddy and Salween River Basins. The analysis also provides some interesting methodological insights about the potential of public macro-scale datasets for environmental assessment. The spatial approach allowed the analysis of different indicators, providing a platform for data integration as well as a visually powerful overview of the study area. Yet, the use of macro-scale datasets entails challenges. Despite improvements, the assessment process tends to be driven by the availability and quality of data, rather than by the actual research and management needs. The greatest utility of macro-scale datasets lies-at least in data-poor areas-in larger scale comparative analyses between the basins and their different sub-areas.

Potential of continuous cover forestry on drained peatlands to increase the carbon sink in Finland.

Land-based mitigation measures are needed to achieve climate targets. One option is the mitigation of currently high greenhouse gas (GHG) emissions of nutrient-rich drained peatland forest soils. Continuous cover forestry (CCF) has been proposed as a measure to manage this GHG emission source; however, its emission reduction potential and impact on timber production at regional and national scales have not been quantified. To quantify the potential emission reduction, we simulated four management scenarios for Finnish forests: (i) The replacement of clear-cutting by selection harvesting on nutrient-rich drained peatlands (CCF) and (ii) the current forest management regime (BAU), and both at two harvest levels, namely (i) the mean annual harvesting (2016-2018) and (ii) the maximum sustainable yield. The simulations were conducted at the stand scale with a forest simulator (MELA) coupled with a hydrological model (SpaFHy), soil C model (Yasso07) and empirical GHG exchange models. Simulations showed that the management scenario that avoided clear-cutting on nutrient-rich drained peatlands (i.e. CCF) produced approximately 1 Tg CO2 eq. higher carbon sinks annually compared with BAU at equal harvest level for Finland. This emission reduction can be attributed to the maintenance of a higher biomass sink and to the mitigation of soil emissions from nutrient-rich drained peatland sites.