AGRICULTURAL BEST MANAGEMENT PRACTICE SENSITIVITY TO CHANGING AIR TEMPERATURE AND PRECIPITATION.

Agricultural best management practices (BMPs) reduce non-point source pollution from cropland. Goals for BMP adoption and expected pollutant load reductions are often specified in water quality management plans to protect and restore waterbodies; however, estimates of needed load reductions and pollutant removal performance of BMPs are generally based on historic climate. Increasing air temperatures and changes in precipitation patterns and intensity are anticipated throughout the U.S. over the 21st century. The effects of such changes on agricultural pollutant loads have been addressed by several authors, but how these changes will affect the performance of widely promoted BMPs has received limited attention. We use the Soil and Water Assessment Tool (SWAT) to investigate potential changes in the effectiveness of conservation tillage, no-till, vegetated filter strips, grassed waterways, nutrient management, winter cover crops, and drainage water management practices under potential future temperature and precipitation patterns. We simulate two agricultural watersheds in the Minnesota Corn Belt and the Georgia Coastal Plain with different hydro-climatic settings, under recent conditions (1950-2005) and multiple potential future mid-century (2030-2059) and late-century (2070-2099) climate scenarios. Results suggest future increases in agricultural source loads of sediment, nitrogen and phosphorous. Most BMPs continue to reduce loads, but removal efficiencies generally decline due to more intense runoff events, biological responses to changes in soil moisture and temperature, and exacerbated upland loading. The coupled effects of higher upland loading and reduced BMP efficiencies suggest that wider adoption, resizing, and/or combining practices may be needed in the future to meet water quality goals for agricultural lands.

The effects of downscaling method on the variability of simulated watershed response to climate change in five U.S. basins.

Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output to develop finer-resolution information for use in water resources impacts assessments. Downscaling, however, can also introduce its own uncertainties into water resources impacts assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). We also assess the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and non-downscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impacts assessments for water - the selection of downscaled climate change information.

Artificial neural network analysis of hydrocarbon profiles for the ageing of Lucilia sericata for post mortem interval estimation.

In analytical chemistry large datasets are collected using a variety of instruments for multiple tasks, where manual analysis can be time-consuming. Ideally, it is desirable to automate this process while obtaining an acceptable level of accuracy, two aims that artificial neural networks (ANNs) can fulfil. ANNs possess the ability to classify novel data based on their knowledge of the domain to which they have been exposed. ANNs can also analyse non-linear data, tolerate noise within data and are capable of reducing time taken to classify large amounts of novel data once trained, making them well-suited to the field of analytical chemistry where large datasets are present (such as that collected from gas chromatography-mass spectrometry (GC-MS)). In this study, the use of ANNs for the autonomous analysis of GC-MS profiles of Lucilia sericata larvae is investigated, where ANNs are required to estimate the age of the larvae to aid in the estimation of the post mortem interval (PMI). Two ANN analysis approaches are presented, where the ANN correctly classified the data with accuracy scores of 80.8% and 87.7% and Cohen's Kappa coefficients of 0.78 and 0.86. Inspection of these results shows the ANN to confuse two consecutive days which are of the same life stage and as a result are very similar in their chemical profile, which can be expected. The grouping of these two days into one class further improved results where accuracy scores 89% and 97.5% were obtained for the two analysis approaches.

Reservoir computing and extreme learning machines for non-linear time-series data analysis.

Random projection architectures such as Echo state networks (ESNs) and Extreme Learning Machines (ELMs) use a network containing a randomly connected hidden layer and train only the output weights, overcoming the problems associated with the complex and computationally demanding training algorithms traditionally used to train neural networks, particularly recurrent neural networks. In this study an ESN is shown to contain an antagonistic trade-off between the amount of non-linear mapping and short-term memory it can exhibit when applied to time-series data which are highly non-linear. To overcome this trade-off a new architecture, Reservoir with Random Static Projections (R(2)SP) is investigated, that is shown to offer a significant improvement in performance. A similar approach using an ELM whose input is presented through a time delay (TD-ELM) is shown to further enhance performance where it significantly outperformed the ESN and R(2)SP as well other architectures when applied to a novel task which allows the short-term memory and non-linearity to be varied. The hard-limiting memory of the TD-ELM appears to be best suited for the data investigated in this study, although ESN-based approaches may offer improved performance when processing data which require a longer fading memory.