Modeling sediment mobilization using a distributed hydrological model coupled with a bank stability model

In addition to surface erosion, stream bank erosion and failure contributes significant sediment and sediment-bound nutrients to receiving waters during high flow events. However, distributed and mechanistic simulation of stream bank sediment contribution to sediment loads in a watershed has not been achieved. Here we present a full coupling of existing distributed watershed and bank stability models and apply the resulting model to the Mad River in central Vermont. We fully coupled the Bank Stability and Toe Erosion Model (BSTEM) with the Distributed Hydrology Soil Vegetation Model (DHSVM) to allow the simulation of stream bank erosion and potential failure in a spatially explicit environment. We demonstrate the model\u27s ability to simulate the impacts of unstable streams on sediment mobilization and transport within a watershed and discuss the model\u27s capability to simulate watershed sediment loading under climate change. The calibrated model simulates total suspended sediment loads and reproduces variability in suspended sediment concentrations at watershed and subbasin outlets. In addition, characteristics such as land use and road-to-stream ratio of subbasins are shown to impact the relative proportions of sediment mobilized by overland erosion, erosion of roads, and stream bank erosion and failure in the subbasins and watershed. This coupled model will advance mechanistic simulation of suspended sediment mobilization and transport from watersheds, which will be particularly valuable for investigating the potential impacts of climate and land use changes, as well as extreme events

Using Multi-Scale Uncertainty Information And Specific Forecast Skill To Improve Reservoir Operations

Optimization of reservoir operations to time series of forecasted inflows are constrained by a set of multiple objectives that span many time scales, however the temporally evolving skill of the forecasts are usually not considered in the objective functions. For example, a flow forecast time series extending from 1 day to 6 months consists of a medium range flow forecast that draws its skill from initial conditions and weather forecasts and a seasonal flow forecast that relies on the initial conditions only. The skill of the medium range flow forecast is the daily and aggregated values with a range of uncertainties that increases with lead time, while the seasonal flow forecasts only have skill in the monthly volumetric values with a range of uncertainties that is large, but predictable. Unfortunately, the impacts of temporally evolving skill and uncertainty on reservoir operations and operational risk is unknown, which may leave significant room for improvement. To explore this question we conduct a set of optimization experiments of reservoir operations at the snowmelt dominated Gunnison River Basin in Colorado and the snow-rain transition Feather River Basin in California. Each optimization experiment uses the same ensemble flow forecast, which is an ensemble medium range forecast merged with an ensemble seasonal forecast, but utilizes a different set of weights that are applied to the medium and seasonal scale objectives (which are to maximize revenue and envrionmental performance). By comparing the weighted set of optimizations to a non-weighted optimization, we are able to isolate the impact of the skill and uncertainty of the forecasts on reservoir operations. We conclude by using the results to develop a functional relationship between the skill and uncertainty in the forecasts to the objective weights and as a basis to calculate operational risk

Modeling the isotopic evolution of snowpack and snowmelt : Testing a spatially distributed parsimonious approach

This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). The Krycklan part of this study was supported by grants from the Knut and Alice Wallenberg Foundation (Branch-points), Swedish Research Council (SITES), SKB and Kempe foundation. The data and model code is available upon request. Authors declare that they have no conflict of interest. We would like to thank the three anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

An approach to understanding hydrologic connectivity on the hillslope and the implications for nutrient transport

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94780/1/gbc975.pd

Testing Above- and Below-Canopy Representations of Turbulent Fluxes in an Energy Balance Snowmelt Model

Turbulent fluxes of sensible and latent heat are important processes in the surface energy balance that drives snowmelt. Modeling these fluxes in a forested environment is complicated because of the canopy effects on the wind field. This paper presents and tests a turbulent flux model developed to represent these processes in an energy balance snowmelt model. The goal is to model these processes using the readily available inputs of canopy height and leaf area index in a way that minimizes the number of parameters, state variables, and assumptions about hard to quantify processes. Selected periods from 9 years of eddy-covariance (EC) measurements at Niwot Ridge, Colorado, were used to evaluate the effectiveness of this modeling approach. The model was able to reproduce the above-canopy sensible and latent heat fluxes reasonably with the correlation higher for sensible heat than latent heat. The modeled values of the below-canopy latent heat fluxes also matched the EC-measured values. The model captured the nighttime below-canopy sensible heat flux quite well, but there were discrepancies in daytime sensible heat flux possibly due to mountain slope circulation not quantifiable in this kind of model. Despite the uncertainties in the below-canopy sensible heat fluxes, the results are encouraging and suggest that reasonable predictions of turbulent flux energy exchanges and subsequent vapor losses from snow in forested environments can be obtained with a parsimonious single-layer representation of the canopy. The model contributes an improved physically based capability for predicting the snow accumulation and melt in a forested environment

Diagnosing a distributed hydrologic model for two high-elevation forested catchments based on detailed stand- and basin-scale data

This study evaluates the performance and internal structure of the distributed hydrology soil vegetation model (DHSVM) using 1998-2001 data collected at Upper Penticton Creek, British Columbia, Canada. It is shown that clear-cut snowmelt rates calculated using data-derived snow albedo curves are in agreement with observed lysimeter outflow. Measurements in a forest stand with 50% air crown closure suggest that the fraction of shortwave radiation transmitted through the canopy is 0.18-0.28 while the hemispherical canopy view factor controlling longwave radiation fluxes to the forest snowpack is estimated at 0.81 ± 0.07. DHSVM overestimates shortwave transmittance (0.50) and underestimates the view factor (0.50). An alternative forest radiation balance is formulated that is consistent with the measurements. This new formulation improves model efficiency in simulating streamflow from 0.84 to 0.91 due to greater early season melt that results from the enhanced importance of longwave radiation below the canopy. The model captures differences in canopy rainfall interception between small and large storms, tree transpiration measured over a 6-day summer period, and differences in soil moisture between a dry and a wet summer. While the model was calibrated to 1999 snow water equivalent (SWE) and hydrograph data for the untreated control basin, it successfully simulates forest and clear-cut SWE and streamflow for the 3 other years and 4 years of preharvesting and postharvesting streamflow for the second basin. Comparison of model states with the large array of observations suggests that the modified model provides a reliable tool for assessing forest management impacts in the region.Mark Thyer, Jos Beckers, Dave Spittlehouse, Younes Alila, and Rita Winkle

Considering Water Availability and Wastewater Resources in the Development of Algal Bio-Oil

This study aims to quantify water appropriation and the potential production of algal bio-oil using freshwater and municipal wastewater effluent (MWW) as an alternative water resource. The county-level analysis focuses on open-pond algae cultivation systems located in 17 states in the southern United States. Several scenarios were developed to examine the water availability for algae bio-oil production under various water resource mixing MWW and freshwater. The results of the analysis indicate that water availability can significantly affect the selection of an algal refinery site and therefore the potential production of algal bio-oil. The production of one liter of algal bio-oil requires 1036–1666 L of water at the state level, in which 3% to 91% can be displaced by MWW, depending on the biorefinery location. This water requirement corresponds to a total of 25 billion liters of bio-oil produced if the spatially and temporally available MWW effluent together with 10% of total available freshwater are used. The production of algal bio-oil is only 14% of estimated production under the assumption that all of the water demand can be fulfilled without any restriction. In addition, if only the spatially and temporally available effluent is used as the sole source of water, the total bio-oil production is estimated to be 9 billion liters. This study not only quantifies the water demands of the algal bio-oil, but it also elucidates the importance of taking water sustainability into account in the development of algal bio-oil

An Analysis on Spatiotemporal Variations of Soil and Vegetation Moisture from a 29 year Satellite Derived Dataset over Mainland Australia

The spatiotemporal behavior of soil and vegetation moisture over mainland Australia was analyzed using passive microwave observations by four satellites going back to late 1978. Differences in measurement specifications prevented merging the data directly. A continuous product was developed for Australia by scaling percentiles of the cumulative moisture distribution within each grid cell to the percentiles of a reference sensor. The coefficient of correlation and root-mean-square error between rescaled values and the reference generally suggest good agreement. Using the merged data product, a strong El Nino-Southern Oscillation signal in near-surface hydrology across Australia was confirmed. Spatial patterns of trends in annual averages show that western and northwestern Australia have experienced an increase in vegetation moisture content, while the east and southeast experienced a decrease. Soil moisture showed a similar spatial pattern but with larger regions experiencing a decrease. This could be explained by decreasing rainfall and increasing potential evapotranspiration during the extended winter period (May-September). The results give us reasonable confidence in the time series of soil and vegetation moisture derived by the scaling method developed in this study. Development of a global data set along these lines should enable better estimation of hydrological variables and should increase understanding of the impacts of ocean circulations on terrestrial hydrology and vegetation dynamics. Copyright 2009 by the American Geophysical Union

Macroalgae Analysis A National GIS-based Analysis of Macroalgae Production Potential Summary Report and Project Plan

The overall project objective is to conduct a strategic analysis to assess the state of macroalgae as a feedstock for biofuels production. The objective in FY11 is to develop a multi-year systematic national assessment to evaluate the U.S. potential for macroalgae production using a GIS-based assessment tool and biophysical growth model developed as part of these activities. The initial model development for both resource assessment and constraints was completed and applied to the demonstration areas. The model for macroalgal growth was extended to the EEZ off the East and West Coasts of the United States, and a plan to merge the findings for an initial composite assessment was developed. In parallel, an assessment of land-based, port, and offshore infrastructure needs based on published and grey literature was conducted. Major information gaps and challenges encountered during this analysis were identified. Also conducted was an analysis of the type of local, state, and federal requirements that pertain to permitting land-based facilities and nearshore/offshore culture operation

Renewable Diesel from Algal Lipids: An Integrated Baseline for Cost, Emissions, and Resource Potential from a Harmonized Model

The U.S. Department of Energy's Biomass Program has begun an initiative to obtain consistent quantitative metrics for algal biofuel production to establish an 'integrated baseline' by harmonizing and combining the Program's national resource assessment (RA), techno-economic analysis (TEA), and life-cycle analysis (LCA) models. The baseline attempts to represent a plausible near-term production scenario with freshwater microalgae growth, extraction of lipids, and conversion via hydroprocessing to produce a renewable diesel (RD) blendstock. Differences in the prior TEA and LCA models were reconciled (harmonized) and the RA model was used to prioritize and select the most favorable consortium of sites that supports production of 5 billion gallons per year of RD. Aligning the TEA and LCA models produced slightly higher costs and emissions compared to the pre-harmonized results. However, after then applying the productivities predicted by the RA model (13 g/m2/d on annual average vs. 25 g/m2/d in the original models), the integrated baseline resulted in markedly higher costs and emissions. The relationship between performance (cost and emissions) and either productivity or lipid fraction was found to be non-linear, and important implications on the TEA and LCA results were observed after introducing seasonal variability from the RA model. Increasing productivity and lipid fraction alone was insufficient to achieve cost and emission targets; however, combined with lower energy, less expensive alternative technology scenarios, emissions and costs were substantially reduced