An optimisation approach for planning preventive drought management measures.

While drought impacts are widespread across the globe, climate change projections indicate more frequent and severe droughts. This underscores the pressing need to increase resistance and resilience to drought. The strategic application of Preventive Drought Management Measures (PDMMs) is a suitable avenue to reduce the likelihood of drought and ameliorate associated damages. In this study, we use an optimisation approach with a multicriteria decision-making method to allocate PDMMs for reducing the severity of agricultural and hydrological droughts. The results indicate that implementing PDMMs can reduce the severity of agricultural and hydrological droughts, and the obtained management scenarios (solutions) highlight the utility of multi-objective optimisation for PDMMs planning. However, examined management scenarios also illustrate the trade-off between managing agricultural and hydrological droughts. PDMMs can alleviate the severity of agricultural droughts while producing opposite effects for hydrological droughts (or vice versa). Furthermore, the impact of PDMMs displays temporal and spatial variabilities. For instance, PDMMs implementation within a specific subbasin may mitigate the severity of one type of drought in a given month yet exacerbate drought conditions in preceding or subsequent months. In the case of hydrological droughts, the PDMMs may intensify streamflow deficits in the intervened subbasins while alleviating the hydrological drought severity downstream (or vice versa). These complexities emphasise a customised implementation of PDMMs, considering the basin characteristics (e.g., rainfall distribution over the year, soil properties, land use, and topography) and the quantification of PDMMs' effect on the severity of each type of drought.

Connecting microbial, nutrient, physiochemical, and land use variables for the evaluation of water quality within mixed use watersheds.

As non-point sources of pollution begin to overtake point sources in watersheds, source identification and complicating variables such as rainfall are growing in importance. Microbial source tracking (MST) allows for identification of fecal contamination sources in watersheds; when combined with data on land use and co-occuring variables (e.g., nutrients, sediment runoff) MST can provide a basis for understanding how to effectively remediate water quality. To determine spatial and temporal trends in microbial contamination and correlations between MST and nutrients, water samples (n = 136) were collected between April 2017 and May of 2018 during eight sampling events from 17 sites in 5 mixed-use watersheds. These samples were analyzed for three MST markers (human - B. theta; bovine - CowM2; porcine - Pig2Bac) along with E. coli, nutrients (nitrogen and phosphorus species), and physiochemical paramaters. These water quality variables were then paired with data on land use, streamflow, precipitation and management practices (e.g., tile drainage, septic tank density, tillage practices) to determine if any significant relationships existed between the observed microbial contamination and these variables. The porcine marker was the only marker that was highly correlated (p value <0.05) with nitrogen and phosphorus species in multiple clustering schemes. Significant relationships were also identified between MST markers and variables that demonstrated temporal trends driven by precipitation and spatial trends driven by septic tanks and management practices (tillage and drainage) when spatial clustering was employed.

Modeling the persistence of viruses in untreated groundwater.

Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled.

Food Footprint as a Measure of Sustainability for Grazing Dairy Farms.

Livestock productions require significant resources allocation in the form of land, water, energy, air, and capital. Meanwhile, owing to increase in the global demand for livestock products, it is wise to consider sustainable livestock practices. In the past few decades, footprints have emerged as indicators for sustainability assessment. In this study, we are introducing a new footprint measure to assess sustainability of a grazing dairy farm while considering carbon, water, energy, and economic impacts of milk production. To achieve this goal, a representative farm was developed based on grazing dairy practices surveys in the State of Michigan, USA. This information was incorporated into the Integrated Farm System Model (IFSM) to estimate the farm carbon, water, energy, and economic impacts and associated footprints for ten different regions in Michigan. A multi-criterion decision-making method called VIKOR was used to determine the overall impacts of the representative farms. This new measure is called the food footprint. Using this new indicator, the most sustainable milk production level (8618 kg/cow/year) was identified that is 19.4% higher than the average milk production (7215 kg/cow/year) in the area of interest. In addition, the most sustainable pasture composition was identified as 90% tall fescue with 10% white clover. The methodology introduced here can be adopted in other regions to improve sustainability by reducing water, energy, and environmental impacts of grazing dairy farms, while maximizing the farm profit and productions.