Forecasting volatility of carbon under EU ETS: a multi-phase study

Environmental Economics and Policy Studies - Carbon management is a strategic priority and organizations need to forecast carbon for that. We aim to find out the best ARIMA-GARCH model for...     

Modeling solute diffusion in the presence of pore-scale heterogeneity: method development and an application to the Culebra dolomite member of the Rustler Formation, New Mexico, USA

Previous studies have revealed the presence of pore-scale variability in diffusivity in the Culebra (dolomite) member of the Rustler Formation, NM. In this study, eight laboratory-scale diffusion experiments on five Culebra samples were analyzed using a methodology for modeling solute diffusion through porous media in the presence of multiple matrix diffusivities, Dp. A lognormal distribution of Dp is assumed within each of the lab samples. The estimated standard deviation (sigma d) of ln(Dp) within each sample ranges from 0 to 1, with most values lying between 0.5 and 1. The variability over all samples leads to a combined sigma d in the range of 1.0-1.2, which is consistent with the distribution of independently determined formation factor measurements for similar Culebra samples. A comparison of our estimation results to other rock properties suggests that, at the lab-scale, the geometric mean of Dp increases with bulk porosity and the quantity of macroscopic features such as vugs and fractures. However, sigma d appears to be determined by variability within such macroscopic features and/or by micropore-scale heterogeneity. In addition, comparison of these experiments to those at larger spatial scales suggests that increasing sample volume results in an increase in sigma d.     

What Matters Most: Are Future Stream Temperatures More Sensitive to Changing Air Temperatures,Discharge, or Riparian Vegetation?

Simulations of stream temperatures showed a wide range of future thermal regimes under a warming climate — from 2.9°C warmer to 7.6°C cooler than current conditions — depending primarily on shade from riparian vegetation. We used the stream temperature model, Heat Source, to analyze a 37‐km study segment of the upper Middle Fork John Day River, located in northeast Oregon, USA. We developed alternative future scenarios based on downscaled projections from climate change models and the composition and structure of native riparian forests. We examined 36 scenarios combining future changes in air temperature (ΔT_air = 0°C, +2°C, and +4°C), stream discharge (ΔQ = ?30%, 0%, and +30%), and riparian vegetation (post‐wildfire with 7% shade, current vegetation with 19% shade, a young‐open forest with 34% shade, and a mature riparian forest with 79% effective shade). Shade from riparian vegetation had the largest influence on stream temperatures, changing the seven‐day average daily maximum temperature (7DADM) from +1°C to ?7°C. In comparison, the 7DADM increased by 1.4°C with a 4°C increase in air temperature and by 0.7°C with a 30% change in discharge. Many streams throughout the interior western United States have been altered in ways that have substantially reduced shade. The effect of restoring shade could result in future stream temperatures that are colder than today, even under a warmer climate with substantially lower late‐summer streamflow.     

Spatial extrapolation of soil characteristics using whole-soil particle size distributions

Soils support ecosystem functions such as plant growth and water quality because of certain physical, chemical, and biological properties. These properties have been studied at different spatial scales, including point scales to satisfy basic research needs, and regional scales to satisfy monitoring needs. Recently, soil property data for the entire USA have become available in the State Soil Geographic Data Base (STATSGO), which is appropriate for regional-scale research. We analyzed and created models of STATSGO data in this study to serve as a research tool, for example, for linking the soil to regional water quality monitoring data in our companion paper. Map units in STATSGO define geographic land areas by soil characteristics (SCs) of similar soil series. We selected 27 SCs that influenced water properties (in varying degrees), aggregated the layer and component SCs to map unit SCs, and used SCs to calculate relationships among map units. The relationships were defined by equations of conditional mean for the qth SC (SCq), while using the remaining 26 SCs as predictors. The relative standard errors for 22 of the 27 SCs were less than 10%, and less than 22% for the remaining five. We conclude that spatial extrapolation of SCs is feasible and the procedures are a first step toward extrapolating information across a region using SC-water property relationships. Although our procedure is for regional scale monitoring, it is also applicable to finer spatial scales commensurate with available soil data.     

Ranch Ownership Change and New Approaches to Water Resource Management in Southwestern Montana: Implications for Fisheries1

Abstract:  This article reports on a survey of ranch owners in high amenity areas in southwestern Montana that have experienced marked ownership change over the last two decades. Specifically, we focus on findings from a set of questions targeting water resource and riparian area management. After reviewing the results, we consider how new owners may be managing water resources differently than longtime owners and what the ecological implications of this shift in management might be for Montana’s prized wild fisheries. Uses more closely associated with new owners than with longtime owners include water reallocation to instream uses, aquatic and riparian ecosystem restoration, and fish pond construction. These uses have both positive and negative impacts on the region’s fisheries. Our findings suggest that current laws and institutions guiding the management of water resources and the aquatic and riparian ecosystems they support may not be adequate to address emerging conservation opportunities and challenges.     

A method for estimating distributions of mass transfer rate coefficients with application to purging and batch experiments

Mass transfer between aquifer material and groundwater is often modeled as first-order rate-limited sorption or diffusive exchange between mobile zones and immobile zones with idealized geometries. Recent improvements in experimental techniques and advances in our understanding of pore-scale heterogeneity demonstrate that two (or even a few) rate coefficients are insufficient in many cases. Here, we investigate a piece-wise linear model for a continuous distribution of rate coefficients, that has several advantages over previously used ‘statistical' distribution models (with functional form from gamma or lognormal PDF's): (1) distributions of arbitrary, even bimodal, shapes can be represented; (2) linear estimation methods can be applied to determine the distribution from experimental data; (3) the uncertainty in the distribution can be determined for each of its sections; and (4) the relationship between the time scales of available data and those of estimatable mass transfer processes can be investigated. A statistical model refinement algorithm is presented that reduces the number of parameters (sections of the piece-wise linear model) to the admissible minimum. We show that purging experiments allow estimation of a wider zone of the rate distribution than do batch experiments, and hence will provide predictions that are accurate over a wider range of time scales. Finally, in an application to TCE gas-purging desorption data, the piece-wise linear rate-distribution model has a higher probability of being adequate than those using a gamma or lognormal distribution or a single rate coefficient.     

Predicting physical and chemical water properties from relationships with watershed soil characteristics

The Surface Waters component of the Environmental Monitoring and Assessment Program (EMAPSW) was developed by the USEPA to evaluate the extent and condition of lakes and streams over national and regional scales. Realistically, chemical or physical water properties (WPs) such as acidity or turbidity can be field-sampled for only a small portion of all lakes and streams. However, soil characteristics (SCs) affect WPs and broad-scale soil survey data have become available in the State Soil Geographic Data Base (STATSGO). We developed models relating observed WPs to SCs to extrapolate the sampled WPs to a region, potentially reducing extensive monitoring needs. Our study region consisted of 13 northeastern and Mid-Atlantic states and contained 882 STATSGO soil map units. We used map units as the spatial component of WP analysis. The WPs were sampled in 721 randomly selected EMAPSW study sites. The watersheds of these sites represent 7.1% of the region's total area and spatially intersect 400 of its soil map units. Each intersected map unit was assigned the weighted average WPs from the corresponding watersheds. Conditional expectation models were used to extrapolate sampled WPs to 882 map units. The relative standard errors ranged from low for pH (0.8%), intermediate for total P (12.1%), and very high for chloride (54.8%). The high extrapolation errors indicate outlier conditions from natural, non-soil, or anthropogenic sources.