A multi-benefit framework for funding forest management in fire-driven ecosystems across the Western U.S.

Forests across the Western U.S. face unprecedented risk due to historic fire exclusion, environmental degradation, and climate change. Forest management activities like ecological thinning, prescribed burning, and meadow restoration can improve landscape resilience. Resilient forests are at a lower risk of high-intensity wildfires, drought, insects, and other disturbances and provide a wide range of benefits to ecosystems and communities. However, insufficient funding limits implementation of critically needed management. To address this challenge, we propose a multi-benefit framework that leverages the diverse benefits of forest management to engage a suite of stakeholders in sharing project costs. We take a three-pronged approach to develop our conceptual model: examining existing frameworks for environmental project implementation, conducting a literature review of forest management benefits, and evaluating case studies. Through our framework, we describe the steps to engage partners, starting by identifying benefits that could accrue to potential public and private beneficiaries, and moving through an iterative and collaborative process of valuing benefits, which can accrue over different spatial and temporal scales, in close consultation with potential beneficiaries themselves. The aim of this approach is to stack funding streams associated with each valued benefit to fully fund a given forest management project. The multi-benefit framework has the potential to unlock new sources of funding to meet the exceptional challenges of climate and wildfire disturbances. We apply the framework to dry forests of the Western U.S., but opportunities exist for expanding and modifying this approach to any geography or ecosystem where management provides multiple benefits.

Effective Remediation of Groundwater Fluoride with Inexpensively Processed Indian Bauxite.

India represents one-third of the world's fluorosis burden and is the fifth global producer of bauxite ore, which has previously been identified as a potential resource for remediating fluoride-contaminated groundwater in impoverished communities. Here, we use thermal activation and/or groundwater acidification to enhance fluoride adsorption by Indian bauxite obtained from Visakhapatnam, an area proximate to endemic fluorosis regions. We compare combinatorial water treatment and bauxite-processing scenarios through batch adsorption experiments, material characterization, and detailed cost analyses. Heating Indian bauxite above 300 °C increases available surface area by > 15× (to ∼170 m2/g) through gibbsite dehydroxylation and reduces the bauxite dose for remediating 10 ppm F- to 1.5 ppm F- by ∼93% (to 21 g/L). Additionally, lowering groundwater pH to 6.0 with HCl or CO2 further reduces the average required bauxite doses by 43-73% for ores heated at 300 °C (∼12 g/L) and 100 °C (∼77 g/L). Product water in most examined treatment scenarios complies with EPA standards for drinking water (e.g., As, Cd, Pb, etc.) but potential leaching of Al, Mn, and Cr is of concern in some scenarios. Among the defluoridation options explored here, bauxite heated at 300 °C in acidified groundwater has the lowest direct costs ($6.86 per person per year) and material-intensity.

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.