MOESHA: A Genetic Algorithm for Automatic Calibration and Estimation of Parameter Uncertainty and Sensitivity of Hydrologic Models.

Characterization of the uncertainty and sensitivity of model parameters is an essential facet of hydrologic modeling. This article introduces the multi-objective evolutionary sensitivity handling algorithm (MOESHA) that combines input parameter uncertainty and sensitivity analyses with a genetic algorithm calibration routine to dynamically sample the parameter space. This novel algorithm serves as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating model parameters to a hydrologic model, MOESHA determines the optimal distribution of model parameters that maximizes model robustness and minimizes error, and the results provide an estimate for model uncertainty due to the uncertainty in model parameters. Subsequently, we compare the model parameter distributions to the distribution of a dummy variable (i.e., a variable that does not affect model output) to differentiate between impactful (i.e., sensitive) and non-impactful parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of model parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrologic model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol's global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters, demonstrate the uncertainty of model results with respect to uncertainties in model parameters, and achieve excellent calibration results. A major drawback of the algorithm is that it is computationally expensive; therefore, parallelized methods should be used to reduce the computational burden.

Gasified Grass and Wood Biochars Facilitate Plant Establishment in Acid Mine Soils.

Heavy metals in exposed mine tailings threaten ecosystems that surround thousands of abandoned mines in the United States. Biochars derived from the pyrolysis or gasification of biomass may serve as a valuable soil amendment to revegetate mine sites. We evaluated the ability of two biochars, produced by gasification of either Kentucky bluegrass seed screenings (KB) or mixed conifer wood (CW), to support the growth of plants in mine spoils from the abandoned Formosa and Almeda Mines in Oregon. To evaluate the potential for plant establishment in mine tailings, wheat was grown in tailings amended with biochar at rates ranging from 0 to 9% (w/w). Both KB and CW biochars promoted plant establishment by increasing soil pH, increasing concentrations of macro- and micronutrients, and decreasing the solubility and plant uptake of heavy metals. Formosa tailings required at least 4% biochar and Almeda soil required at least 2% biochar to promote healthy wheat growth. A complimentary experiment in which mine spoils were leached with simulated precipitation indicated that biochar amendment rates ≥4% were sufficient to neutralize the elution pH and reduce concentrations of potentially toxic elements (Zn, Cu, Ni, Al) to levels near or below concern. These findings support the use of gasified biochar amendments to revegetate acid mine soils.

Cost of areal reduction of gulf hypoxia through agricultural practice.

A major share of the area of hypoxic growth in the Northern Gulf of Mexico has been attributed to nutrient run-off from agricultural fields, but no estimate is available for the cost of reducing Gulf hypoxic area using agricultural conservation practices. We apply the Soil and Water Assessment Tool using observed daily weather to simulate the reduction in nitrogen loading in the Upper Mississippi River Basin (UMRB) that would result from enrolling all row crop acreage in the Conservation Reserve Program (CRP). Nitrogen loadings at the outlet of the UMRB are used to predict Gulf hypoxic area, and net cash farm rent is used as the price for participation in the CRP. Over the course of the 42 year simulation, direct CRP costs total more than $388 billion, and the Inter-Governmental Task Force goal of hypoxic area less than 5000 square kilometers is met in only two years.

Fatty acid methyl ester analysis to identify sources of soil in surface water.

Efforts to improve land-use practices to prevent contamination of surface waters with soil are limited by an inability to identify the primary sources of soil present in these waters. We evaluated the utility of fatty acid methyl ester (FAME) profiles of dry reference soils for multivariate statistical classification of soils collected from surface waters adjacent to agricultural production fields and a wooded riparian zone. Trials that compared approaches to concentrate soil from surface water showed that aluminum sulfate precipitation provided comparable yields to that obtained by vacuum filtration and was more suitable for handling large numbers of samples. Fatty acid methyl ester profiles were developed from reference soils collected from contrasting land uses in different seasons to determine whether specific fatty acids would consistently serve as variables in multivariate statistical analyses to permit reliable classification of soils. We used a Bayesian method and an independent iterative process to select appropriate fatty acids and found that variable selection was strongly impacted by the season during which soil was collected. The apparent seasonal variation in the occurrence of marker fatty acids in FAME profiles from reference soils prevented preparation of a standardized set of variables. Nevertheless, accurate classification of soil in surface water was achieved utilizing fatty acid variables identified in seasonally matched reference soils. Correlation analysis of entire chromatograms and subsequent discriminant analyses utilizing a restricted number of fatty acid variables showed that FAME profiles of soils exposed to the aquatic environment still had utility for classification at least 1 wk after submersion.