Citation:

Macedo, D., R. Hughes, Philip R. Kaufmann, AND M. Callisto. Development and Validation of an Environmental Fragility Index (EFI) for the Neotropical Savannah Biome. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 635:1267-1279, (2018). https://doi.org/10.1016/j.scitotenv.2018.04.216

Impact/Purpose:

Fine sediment delivery to streams from upland land-use impairs habitat and biota in streams. Further downstream, reservoirs receiving sediment-laden runoff from these streams can lose useful water storage capacity. In this article, the authors develop an environmental fragility index (EFI) to evaluate the general level of fine sediment delivery to streams in the Neotropical Savannah, the second largest biome in Brazil. This is one of the world’s most threatened biomes, suffering from replacement of natural vegetation with crops, pastures, and urbanization. Furthermore, most Brazilian hydropower facilities are located in this biome, which diminish native biodiversity and ecological integrity, but in return provide such ecosystem services as hydroelectric power generation, water supply, and recreation. Anthropogenic pressures in catchments export excess sediment and other pollutants downstream, threatening the operation and service life of existing reservoirs, thereby necessitating the construction of new reservoirs that will further diminish biodiversity and ecosystem services associated with un-impounded river systems. The EFI was calculated using GIS data on geoclimatic controls on sediment production (rainfall, variation of elevation and slope, geology) and anthropogenic pressures (natural cover, road density, distance from roads and urban centers) in the catchments upstream of four large hydroelectric reservoirs. The EFI was formulated to optimally predict a field-derived measure of relative bed stability (LRBS), an indicator of excess fine sediments in streams draining into these reservoirs. The LRBS measure was calculated from field data collected in Brazil using survey designs and field sampling methods developed by the USEPA, and currently used by the National Aquatic Resource Surveys (NARS). The EFI can be used to aid conservation and reservoir basin rehabilitation and mitigation projects, as well as evaluating the relative merits of agricultural and urban land use management options. Although applied in Brazil, this adaptation of US EPA’s National Aquatic Resource Survey (NARS) designs and methods are not only valuable tests of the NARS approaches, but contribute to environmental science, resource management, and conservation of biodiversity through new understandings of natural and anthropogenic controls on biota and physical habitat in streams. The model-based predictions of field-derived stream attributes using GIS coverages in combination with field-derived measures of ecological condition demonstrated in this article hold great potential for application throughout the U.S. and internationally.

Description:

Currently, several studies highlight the importance of ecosystem services, including those related to water resources, such as water supply, irrigation, recreation and power generation. Augmented production and transport of fine sediments resulting from increased human activities are major threats to freshwater ecosystems, including reservoirs and their ecosystem services. To support large scale assessment of the likelihood of soil erosion and reservoir sedimentation, we developed and validated an environmental fragility index (EFI) for the neotropical savannah, the second largest Brazilian biome. The EFI was derived from measured geoclimatic controls on sediment production (rainfall, variation of elevation and slope, geology) and anthropogenic pressures (natural cover, road density, distance from roads and urban centers) in 111 catchments upstream of four large hydroelectric reservoirs. We evaluated the effectiveness of the EFI by regressing it against a relative bed stability index (LRBS) that assesses the degree to which stream sites draining into the reservoirs are affected by excess sediments. We developed the EFI on 111 of these sites and validated our model on the remaining 37 independent sites. We also compared the effectiveness EFI in predicting LRBS with that of a multiple linear regression model (via best-subset procedure) using 7 independent variables. The EFI was significantly correlated with the LRBS, with regression R2 values of 0.32 and 0.40 (p <0.001), respectively, in development and validation sites. Although the EFI and multiple regression explained similar amounts of variability (R2 = 0.32 vs 0.36), the EFI had a higher F-ratio (51.6 vs 8.5) and better AICc value (333 vs 338). Because the sites were randomly selected and well-distributed across geoclimatic controlling factors, we were able to calculate spatially-explicit EFI values for all hydrologic units within the study area (~ 38,500 km2). This model-based inference showed that over 65% of those units had high or extreme fragility, and that two of the reservoirs were at much higher risk for sedimentation than the other two. This methodology proved to be efficient and inexpensive, with great potential for application in the management, recovery, and preservation of hydroelectric reservoirs and streams in tropical river basins of Brazil, and likely in other parts of the world.

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