Amazon forest biomass density maps: tackling the uncertainty in carbon emission estimates

As land use change (LUC), including deforestation, is a patchy process, estimating the impact of LUC on carbon emissions requires spatially accurate underlying data on biomass distribution and change. The methods currently adopted to estimate the spatial variation of above- and below-ground biomass in tropical forests, in particular the Brazilian Amazon, are usually based on remote sensing analyses coupled with field datasets, which tend to be relatively scarce and often limited in their spatial distribution. There are notable differences among the resulting biomass maps found in the literature. These differences subsequently result in relatively high uncertainties in the carbon emissions calculated from land use change, and have a larger impact when biomass maps are coded into biomass classes referring to specific ranges of biomass values. In this paper we analyze the differences among recently-published biomass maps of the Amazon region, including the official information used by the Brazilian government for its communication to the United Nation Framework on Climate Change Convention of the United Nations. The estimated average pre-deforestation biomass in the four maps, for the areas of the Amazon region that had been deforested during the 1990–2009 period, varied from 205?±?32 Mg ha^?1 during 1990–1999, to 216?±?31 Mg ha^?1 during 2000–2009. The biomass values of the deforested areas in 2011 were between 7 and 24 % higher than for the average deforested areas during 1990–1999, suggesting that although there was variation in the mean value, deforestation was tending to occur in increasingly carbon-dense areas, with consequences for carbon emissions. To summarize, our key findings were: (i) the current maps of Amazonian biomass show substantial variation in both total biomass and its spatial distribution; (ii) carbon emissions estimates from deforestation are highly dependent on the spatial distribution of biomass as determined by any single biomass map, and on the deforestation process itself; (iii) future deforestation in the Brazilian Amazon is likely to affect forests with higher biomass than those deforested in the past, resulting in smaller reductions in carbon dioxide emissions than expected purely from the recent reductions in deforestation rates; and (iv) the current official estimate of carbon emissions from Amazonian deforestation is probably overestimated, because the recent loss of higher-biomass forests has not been taken into account.     

Floods and livelihoods: The impact of changing water resources on wetland agro-ecological production systems in the Tana River Delta,Kenya

Wetlands are highly dynamic and productive systems that have been under increased pressure from changes in land use and water management strategies. In Eastern Africa, wetlands provide resources at multiple spatial and temporal levels through farming, fishing, livestock ownership and a host of other ecosystem services that sustain the local economy and individual livelihoods. As part of a broader effort to describe future development scenarios for East African coastal wetlands, this qualitative study focuses on understanding the processes by which river water depletion has affected local food production systems in Kenya's Tana River Delta over the past 50 years, and how this situation has impacted residents’ livelihoods and well-being. Interviews performed in six villages among various ethnic groups, geographical locations and resource profiles indicated that the agro-ecological production systems formerly in place were adapted to the river's dynamic flooding patterns. As these flooding patterns changed, the local population diversified and abandoned or adopted various farming, fishing and livestock-rearing techniques. Despite these efforts, the decrease in water availability affected each subcomponent of the production systems under study, which led to their collapse in the 1990s. Water depletion negatively impacted local human well-being through the loss of food security. The current study provides a detailed account of the dynamics of agro-ecological production systems facing the effects of river water depletion in a wetland-associated environment in Sub-Saharan Africa.     

Influence d'un changement climatique dû à une hausse de gaz à effet de serre sur l'agriculture au quebec

Abstract

This study, using a climate change scenario generated by the Canadian Climate Centre (CCC) general circulation model (GCM) examines the impacts of such a climate change on agriculture in southern Quebec. Using a crop model from the Food and Agriculture Organization (FAO), yield responses of a variety of cereals, legumes, oleaginous and special crops to climate change are analysed and discussed.

Results show that under the 2 × CO₂ climate scenario the growing season would be longer and accumulation of corn heat units and growing degree days would be more important than under actual climate (1961–1990). One of the more important results of this study is that, on the one hand yield of C₃ cereals would be lower and that of C₄ cereals higher in most agricultural regions. On the other hand, the direct fertilisation effect of increased CO₂ is not considered. It must be cautioned however that we can not generalise results obtained for one legume crop to all legumes.     

Climate and climate change in western canada as simulated by the Canadian regional climate model

Abstract

A þrst climate simulation performed with the novel Canadian Regional Climate Model (CRCM) is presented. The CRCM is based on fully elastic non‐hydrostatic þeld equations, which are solved with an efþcient semi‐implicit semi‐Lagrangian (SISL) marching algorithm, and on the parametrization package of subgrid‐scale physical effects of the second‐generation Canadian Global Climate Model (GCMII). Two 5‐year integrations of the CRCM nested with GCMII simulated data as lateral boundary conditions are made for conditions corresponding to current and doubled CO₂ scenarios. For these simulations the CRCM used a grid size of 45 km on a polar‐stereographic projection, 20 scaled‐height levels and a time step of 15 min; the nesting GCMII has a spectral truncation of T32, 10 hybrid‐pressure levels and a time step of 20 min. These simulations serve to document: (1) the suitability of the SISL numerical scheme for regional climate modelling, (2) the use of GCMII physics at much higher resolution than in the nesting model, (3) the ability of the CRCM to add realistic regional‐scale climate information to global model simulations, and (4) the climate of the CRCM compared to that of GCMII under two greenhouse gases (GHG) scenarios.     

Modeling the environmental susceptibility of landfill sites in California

??The proper management of solid waste (SW) is a global environmental challenge. A major issue is the proper disposal of SW while balancing a wide range of criteria and working with different spatial data. In this study, we used geographic information system as a tool to perform multi-criteria decision analysis with an analytical hierarchy process to develop an environmental impact susceptibility model (EISM) for landfills. The model was applied to the state of California, USA and results are presented herein. In particular, the EISM considers factors such as geology, pedology, geomorphology, water resources, and climate as represented by 13 associated environmental indicators. The results of the EISM indicate that more than 75% of California’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 25% of the state’s land, 61 landfills are located in the high and very high categories. These results are alarming because during the period from 2000 to 2015, these 61 landfills received approximately 308 million tons of SW, which corresponds to more than 57% of all SW disposed in California. The model results can be used toward mitigating the environmental impacts of these facilities.     

Morphological similarities between DBM and a microeconomic model of sprawl

We present a model that simulates the growth of a metropolitan area on a 2D lattice. The model is dynamic and based on microeconomics. Households show preferences for nearby open spaces and neighbourhood density. They compete on the land market. They travel along a road network to access the CBD. A planner ensures the connectedness and maintenance of the road network. The spatial pattern of houses, green spaces and road network self-organises, emerging from agents individualistic decisions. We perform several simulations and vary residential preferences. Our results show morphologies and transition phases that are similar to Dieletric Breakdown Models (DBM). Such similarities were observed earlier by other authors, but we show here that it can be deducted from the functioning of the land market and thus explicitly connected to urban economic theory.     

Long-term groundwater resource observatory for Southwestern Madagascar

Madagascar has one of the highest poverty rates in the world and consequently the long-term monitoring of groundwater resources is not a priority for the authorities. However, groundwater is often the only sustainable resource that has a satisfactory quality to supply the population. This is especially true in the south-west of the country, which is a semi-arid region and a global change hot spot (intense land use and climate changes). In response to the lack of data, the Groundwater Resource Observatory for Southwestern Madagascar (GROSoM) was established to monitor piezometry and meteorology over the longer term as part of a humanitarian response. The first site was setup in 2014 in a catchment located over a carbonate plateau; in 2018, a second site was installed in an alluvial setting within a crystalline basement catchment and a third site will be installed in 2020 to monitor groundwater dynamics in a coastal setting. The three sites, located between Toliara and Taolagnaro cities, are complementary and representative of various hydrogeological systems in Southwestern Madagascar. Each site includes a weather station and between 3 and 6 piezometric probes. The monitoring data indicate a strong inter-annual variability in precipitation, which induces a strong variability in aquifers recharge. One of the driest years in 2016 seems to be consistent with strong El Niño – Southern Oscillation (ENSO) effects observed at the global scale, while years with higher recharge appear to be related to cyclones such as Fundi in 2015 and Eketsang in 2019. Preliminary results of cross-disciplinary studies demonstrated a link between groundwater and health issues (i.e., admissions to basic health centres). This observatory aims to produce long-term data and has two objectives: (i) strengthening the early warning system for humanitarian crises in Madagascar; (ii) contributing to a better understanding of the effects of climate change on groundwater resources in this semi-arid region.     

Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010

We compared the interannual variability of annual daily maximum and minimum extreme water levels in Lake Ontario and the St Lawrence River (Sorel station) from 1918 to 2010, using several statistical tests. The interannual variability of annual daily maximum extreme water levels in Lake Ontario is characterized by a positive long‐term trend showing two shifts in mean (1929–1930 and 1942–1943) and a single shift in variance (in 1958–1959). In contrast, for the St Lawrence River, this interannual variability is characterized by a negative long‐term trend with a single shift in mean, which occurred in 1955–1956. As for annual daily minimum extreme water levels, their interannual variability shows no significant long‐term change in trend. However, for Lake Ontario, the interannual variability of these water levels shows two shifts in mean, which are synchronous with those for maximum water levels, and a single shift in variance, which occurred in 1965–1966. These changes in trend and stationarity (mean and variance) are thought to be due to factors both climatic (the Great Drought of the 1930s) and human (digging of the Seaway and construction of several dams and locks during the 1950s). Despite this change in means and variance, the four series are clearly described by the generalized extreme value distribution. Finally, annual daily maximum and minimum extreme water levels in the St Lawrence and Lake Ontario are negatively correlated with Atlantic multidecadal oscillation over the period from 1918 to 2010. Copyright © 2013 John Wiley & Sons, Ltd.