The Mekong River is a globally important river system, known for its unique flood pulse hydrology, ecological productivity, and biodiversity. Flooded forests provide critical terrestrial nutrient inputs and habitat to support aquatic species. However, the Mekong River is under threat from anthropogenic stressors, including deforestation from land cultivation and urbanization, and dam construction that inundates forests and encourages road development. This study investigated spatio-temporal patterns of deforestation in Cambodia and portions of neighboring Laos and Vietnam that form the Srepok–Sesan–Sekong watershed. A random forest model predicted tree cover change over a 25-year period (1993–2017) using the Landsat satellite archive. Then, a statistical predictive deforestation model was developed using annual-resolution predictors such as land-cover change, hydropower development, forest fragmentation, and socio-economic, topo-edaphic and climatic predictors. The results show that almost 19% of primary forest (nearly 24,000 km2) was lost, with more deforestation in floodplain (31%) than upland (18%) areas. Our results corroborate studies showing extremely high rates of deforestation in Cambodia. Given the rapidly accelerating deforestation rates, even in protected areas and community forests, influenced by a growing population and economy and extreme poverty, our study highlights landscape features indicating an increased risk of future deforestation, supporting a spatial framework for future conservation and mitigation efforts.

The Soil Vulnerability Index (SVI) was developed by the USDA Natural Resources Conservation Service (NRCS) to classify inherent vulnerability of cropland soils based on field sediment and nutrient transport resulting from surface runoff and leaching. The primary purpose of the SVI is to aid conservation planners in more rapidly assessing managed lands and inherent resource concerns. The index is based on hydrologic soil group, slope, and soil erodibility for cultivated cropland soils, with the addition of percentage rock fragments and organic matter when considering leaching. Although the SVI is intended for use throughout the United States, its development was based on the physiographic and rainfall characteristics of the Upper Mississippi and Ohio-Tennessee River basins. The purpose of this study was to evaluate the SVI in areas both in and outside of the area for which it was developed. Thirteen different watersheds were selected to conduct this evaluation. Vulnerability classifications using the SVI were compared with those from on-site experts' knowledge and with model simulations using local data. Four companion papers in this special collection discuss SVI classification based on the effects of land slope, artificial drainage, sediment and nutrient loads, and vulnerability assessment using hydrologic simulation models. Using results from the various sites, the objective of this paper was to synthesize the interpretation of the value and applicability of SVI vulnerability classification to sediment and nutrient loss across various physiographic regions and suggest where improvement in the SVI could be made.

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There is an increasing need to quickly and accurately identify areas where agricultural conservation practices can provide the greatest reduction in nutrient and sediment runoff. Geographic information systems (GIS)-based tools and indices are promising for identifying critical areas that are significant contributors of nonpoint source pollution loads with limited data. One such tool, the Soil Vulnerability Index (SVI), is tested here in Beasley Lake and Goodwin Creek watersheds in Mississippi. The SVI runoff component results are compared against aerial images and long-term land use histories in the watershed to determine if a higher SVI score is related to visibly degraded land or land removed from cultivation. SVI results are also compared to sediment yield estimates generated with the Annualized Agricultural Non-Point Source pollution model (AnnAGNPS) to determine the degree of agreement. The SVI runoff score demonstrated agreement with imagery and land use histories in both watersheds. The SVI categories and corresponding AnnAGNPS-predicted sediment yield also had moderate agreement, with 45% and 68% of watershed area in agreement in Beasley Lake and Goodwin Creek watersheds, respectively. In general, the tool is a quick way to assess spatial areas potentially contributing to nonpoint source pollution, which can then be combined with field-based knowledge and/or imagery to provide valuable insight for placement of conservation practices.