The relative contribution of peat compaction and oxidation to subsidence in built-up areas in the Rhine-Meuse delta, The Netherlands.

An increasing number of people lives in coastal zones with a subsurface consisting of heterogenic soft-soil sequences. Many of these sequences contain substantial amounts of peat. While population growth and urbanization continues in coastal zones, they are threatened by global sea-level rise and land subsidence. Peat compaction and oxidation, caused by loading and drainage, are important contributors to land subsidence, and hence relative sea-level rise, in peat-rich coastal zones. Especially built-up areas, having densely-spaced urban assets, are heavily impacted by land subsidence, in terms of livelihoods and damage-related costs. Yet, built-up areas have been largely avoided in peat compaction and oxidation field studies. Consequently, essential information on the relative contributions of both processes to total subsidence and underlying mechanisms, which is required for developing effective land use planning strategies, is lacking. Therefore, we quantified subsidence due to peat compaction and oxidation in built-up areas in the Rhine-Meuse delta, The Netherlands, using lithological borehole data and measurements of dry bulk density, organic matter, and CO2 respiration. We reconstructed subsidence over the last 1000 years of up to ~4 m, and recent subsidence rates of up to ~140 mm·yr-1 averaged over an 11-year time span. The amount and rate of subsidence due to peat compaction and oxidation is variable in time and space, depending on the Holocene sequence composition, overburden thickness, loading time, organic-matter content, and groundwater-table depth. In our study area, the potential for future subsidence due to peat compaction and oxidation is substantial, especially where the peat layer occurs at shallow depth and is relatively uncompacted. We expect this is the case for many peat-rich coastal zones worldwide. We propose to use subsurface-based spatial planning, using specific subsurface information mentioned above, to inform land use planners about the most optimal building sites in organo-clastic coastal zones.

The impact of avulsion on groundwater level and peat formation in delta floodbasins during the middle-Holocene transgression in the Rhine-Meuse delta, The Netherlands.

By redistributing water and sediment in delta plains, avulsions of river branches have major environmental impacts, notably in changing hydrological and peat-forming conditions in floodbasins. The central part of the Rhine-Meuse delta, with its extensive databases including detailed lithological data and high-resolution age control, offers a unique opportunity to study middle-Holocene avulsion impacts on floodbasin groundwater level and peat formation. Avulsion has caused local accelerations of rising groundwater tables to be superimposed on decelerating base-level rise. This is evident from comparing single-site groundwater rise for multiple floodbasins in the river-dominated part of the delta, with regionally averaged groundwater-rise reconstructions. Floodbasin type (lacustrine versus terrestrial wetland), size and openness, partly through effects on discharge dispersal, affect how strongly the floodbasin groundwater tables respond to avulsion-diverted discharge. Cross-sectional lithology repeatedly indicates a shift from high-organic wood peat to low-organic reed peat in the vicinity of the avulsed channel, resulting from changes in water-table regime and nutrient status. Avulsive impact on the floodbasin groundwater table was most pronounced during the transition from transgressive to high-stand stage (between ca. 6000 and 4000 years ago), owing to developing floodbasin compartmentalization (size reduction, confinement) resulting from repeated avulsion. By way of environmental impacts on groundwater tables and vegetation, avulsions thus affect the heterogeneity of floodbasin facies.

Drivers of wetland conversion: a global meta-analysis.

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Land cover change or land-use intensification: simulating land system change with a global-scale land change model.

Land-use change is both a cause and consequence of many biophysical and socioeconomic changes. The CLUMondo model provides an innovative approach for global land-use change modeling to support integrated assessments. Demands for goods and services are, in the model, supplied by a variety of land systems that are characterized by their land cover mosaic, the agricultural management intensity, and livestock. Land system changes are simulated by the model, driven by regional demand for goods and influenced by local factors that either constrain or promote land system conversion. A characteristic of the new model is the endogenous simulation of intensification of agricultural management versus expansion of arable land, and urban versus rural settlements expansion based on land availability in the neighborhood of the location. Model results for the OECD Environmental Outlook scenario show that allocation of increased agricultural production by either management intensification or area expansion varies both among and within world regions, providing useful insight into the land sparing versus land sharing debate. The land system approach allows the inclusion of different types of demand for goods and services from the land system as a driving factor of land system change. Simulation results are compared to observed changes over the 1970-2000 period and projections of other global and regional land change models.

A Land System representation for global assessments and land-use modeling.

Current global scale land-change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land-use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human-environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi-)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land-use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land-change models that include the human drivers of land change are best parameterized at sub-global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.