BackgroundThe degradation of forests in developing countries, particularly those within tropical and subtropical latitudes, is perceived to be an important contributor to global greenhouse gas emissions. However, the impacts of forest degradation are understudied and poorly understood, largely because international emission reduction programs have focused on deforestation, which is easier to detect and thus more readily monitored. To better understand and seize opportunities for addressing climate change it will be essential to improve knowledge of greenhouse gas emissions from forest degradation.ResultsHere we provide a consistent estimation of forest degradation emissions between 2005 and 2010 across 74 developing countries covering 2.2 billion hectares of forests. We estimated annual emissions of 2.1 billion tons of carbon dioxide, of which 53% were derived from timber harvest, 30% from woodfuel harvest and 17% from forest fire. These percentages differed by region: timber harvest was as high as 69% in South and Central America and just 31% in Africa; woodfuel harvest was 35% in Asia, and just 10% in South and Central America; and fire ranged from 33% in Africa to only 5% in Asia. Of the total emissions from deforestation and forest degradation, forest degradation accounted for 25%. In 28 of the 74 countries, emissions from forest degradation exceeded those from deforestation.ConclusionsThe results of this study clearly demonstrate the importance of accounting greenhouse gases from forest degradation by human activities. The scale of emissions presented indicates that the exclusion of forest degradation from national and international GHG accounting is distorting. This work helps identify where emissions are likely significant, but policy developments are needed to guide when and how accounting should be undertaken. Furthermore, ongoing research is needed to create and enhance cost-effective accounting approaches.
The hydrological consequences of wildfires are among their most significant and long-lasting effects. As wildfire severity affects post-fire hydrological response, fuel treatments can be a useful tool for land managers to moderate this response. However, current models focus on only one aspect of the fire–watershed linkage (fuel treatments, fire behaviour, fire severity, watershed responses). This study outlines a spatial modelling approach that couples three models used sequentially to allow managers to model the effects of fuel treatments on post-fire hydrological responses. Case studies involving a planned prescribed fire at Zion National Park and a planned mechanical thinning at Bryce Canyon National Park were used to demonstrate the approach. Fuel treatments were modelled using FuelCalc and FlamMap within the Wildland Fire Assessment Tool (WFAT). The First Order Fire Effects Model (FOFEM) within WFAT was then used to evaluate the effectiveness of the fuel treatments by modelling wildfires on both treated and untreated landscapes. Post-wildfire hydrological response was then modelled using KINEROS2 within the Automated Geospatial Watershed Assessment tool (AGWA). This coupled model approach could help managers estimate the effect of planned fuel treatments on wildfire severity and post-wildfire runoff or erosion, and compare various fuel treatment scenarios to optimise resources and maximise mitigation results.
Representation of precipitation is one of the most difficult aspects of modelling post-fire runoff and erosion and also one of the most sensitive input parameters to rainfall-runoff models. The impact of post-fire convective rainstorms, especially in semiarid watersheds, depends on the overlap between locations of high-intensity rainfall and areas of high-severity burns. One of the most useful applications of models in post-fire situations is risk assessment to quantify peak flow and identify areas at high risk of flooding and erosion. This study used the KINEROS2/AGWA model to compare several spatial and temporal rainfall representations of post-fire rainfall-runoff events to determine the effect of differing representations on modelled peak flow and determine at-risk locations within a watershed. Post-fire rainfall-runoff events at Zion National Park in Utah and Bandelier National Monument in New Mexico were modelled. Representations considered included both uniform and Soil Conservation Service Type II hyetographs, applying rain over the entire watershed and applying rain only on the burned area, and varying rainfall both temporally and spatially according to radar data. Results showed that rainfall representation greatly affected modelled peak flow, but did not significantly alter the model’s predictions for high-risk locations. This has important implications for post-fire assessments before a flood-inducing rainfall event, or for post-storm assessments in areas with low-gauge density or lack of radar data due to mountain beam blockage.
In the Lower Mekong River Basin (LMB), deforestation rates are some of the highest in the world as land is converted primarily into intensive agriculture and plantations. While this has been a key for the region’s economic development, rural populations dependent on the freshwater water resources that support their fishing and agriculture industries are increasingly vulnerable to the impacts of flood, drought and non-point source pollution. Impacts of deforestation on ecosystem services (ES) including hydrological ES that control the availability and quality of fresh water across the landscape, regulating floods and droughts, soil erosion and non-point source pollution are known. Despite this understanding at the hillslope level, few studies have been able to quantify the impact of wide-scale deforestation on larger tropical watersheds. This study introduces a new methodology to quantify the impact of deforestation on water-based ES in the LMB with a focus on Cambodia by combining spatial datasets on forest loss from remote sensing and spatially-explicit hydrological modeling. Numerous global and regional remote sensing products are synthesized to develop detailed land use change maps for 2001 to 2013 for the LMB, which are then used as inputs into a hydrological model to develop unique spatial datasets that map ES changes due to deforestation across the LMB. The results point to a clear correlation between forest loss and surface runoff, with a weaker but upward trending relationship between forest loss and sediment yield. This resulted in increased river discharge for 17 of the 22 watersheds, and increased sediment for all 22 watersheds. While there is considerable variability between watersheds, these results could be helpful for prioritizing interventions to decrease deforestation by highlighting which areas have experienced the greatest change in water-based ES provision. These results are also presented in a web-based platform called the Watershed Ecosystem Service Tool.
Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.
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