A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Does the Temporal Resolution of Precipitation Input Influence the Simulated Hydrological Components Employing the SWAT Model?

This study aimed to evaluate the influence of sub‐daily precipitation time steps on model performance and hydrological components by applying the Green and Ampt infiltration method using the Soil and Water Assessment Tool (SWAT). Precipitation was measured at a resolution of 0.1 mm and aggregated to 5‐, 15‐, 30‐, and 60‐min time steps. Daily discharge data over a 10‐year period were used to calibrate and validate the model. Following a global sensitivity analysis, relevant parameters were optimized through an automatic calibration procedure using SWAT‐CUP for each time step. Daily performance statistics were almost equal among all four time steps (NSE ≈ 0.47). Discharge mainly consisted of groundwater flow (55%) and tile flow (42%), in reasonable proportions for the investigated catchment. In conclusion, model outputs were almost identical, showing simulations responded nearly independently of the chosen precipitation time step. This held true for (1) the selection of sensitive parameters, (2) performance statistics, (3) the shape of the hydrographs, and (4) flow components. However, a scenario analysis revealed that the precipitation time step becomes important when saturated hydraulic conductivities are low and curve numbers are high. The study suggests that there is no need in using precipitation time steps <1 h for lowland catchments dominated by soils with a low surface runoff potential if daily flow values are being considered. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Evaluation of Existing and Modified Wetland Equations in the SWAT Model

The ability to accurately simulate flow and nutrient removal in treatment wetlands within an agricultural, watershed‐scale model is needed to develop effective plans for meeting nutrient reduction goals associated with protection of drinking water supplies and reduction of the Gulf of Mexico hypoxic zone. The objectives of this study were to incorporate new equations for wetland hydrology and nutrient removal in Soil and Water Assessment Tool (SWAT), compare model performance using original and improved equations, and evaluate the ramifications of errors in watershed and tile drain simulation on prediction of NO₃‐N dynamics in wetlands. The modified equations produced Nash‐Sutcliffe Efficiency values of 0.88 to 0.99 for daily NO₃‐N load predictions, and percent bias values generally less than 6%. However, statistical improvement over the original equations was marginal and both old and new equations provided accurate simulations. The new equations reduce the model's dependence on detailed monitoring data and hydrologic calibration. Additionally, the modified equations increase SWAT's versatility by incorporating a weir equation and an irreducible nutrient concentration and temperature coefficient. Model improvements enhance the utility of SWAT for simulating flow and nutrients in wetlands and other impoundments, although performance is limited by the accuracy of inflow and NO₃‐N predictions from the contributing watershed. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices

The high‐performance liquid‐chromatographic retentions of red‐wine pesticide residues are modeled by structure–property relationships. The effect of different types of features is analyzed: geometric, lipophilic, etc. The properties are fractal dimensions, partition coefficient, etc., in linear and nonlinear correlation models. Biological plastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among the principles of evolutionary indeterminacy, morphological determination and natural selection. It is applied to design the co‐ordination index that is used to characterize pesticide retentions. The parameters used to calculate the co‐ordination index are the molar formation enthalpy, molecular weight and surface area. The morphological and co‐ordination indices barely improve the correlations. The fractal dimension averaged for non?buried atoms, partition coefficient, etc. distinguishes the pesticide molecular structures. The structural and constituent classification is based on nonplanarity, and the number of cycles, and O, S, N and Cl atoms. Different behavior depends on the number of cycles.     

Persistence and biodegradation of carbaryl in soils

Abstract

The persistence of the methylcarbamate pesticide carbaryl was studied in four soils under flooded conditions. A substantial portion of the pesticide was recovered from all soils even after 15 days of its application, with the recovery ranging from 37% in an alluvial soil to 73% in an acid sulfate soil. The degradation of carbaryl was more rapid under flooded conditions than under nonflooded conditions. A bacterium, Pseudomonas cepacia, isolated from a flooded soil amended with a related methylcarbamate pesticide carbofuran, degraded carbaryl in a mineral medium supplemented with yeast extract.     

Field application of evaluated methodology for determining 1,2‐dibromoethane,EDB, in ambient air

Abstract

EDB (ethylene dibromide) is of regulatory interest because it has cancer inducing properties and is a causative agent of aspermia. Previously evaluated methodology was used to determine the extent of exposure of workers to EDB in citrus fumigation stations and a warehouse used as a holding site before shipment. The purpose of this effort was to carry out environmental sampling, and to determine the exposure level of workers and related administrative personnel at two citrus fumigation centers and at a warehouse     

Induction of hepatic drug metabolizing enzymes in mammals by pesticides: A review

Abstract

The induction of drug metabolizing enzymes in mammals is summarized including both enzymes of the cytochrome P‐450‐dependent microsomal mixed function oxidase system and glutathione S‐transferases. Particular emphasis is placed on the role of pesticides as inducers, the early work being summarized while investigations carried out at North Carolina State University are considered in greater detail. Finally, the possible significance of induction is considered.     

A case study of REDD+ challenges in the post‐2012 climate regime: The scenarios approach

The REDD+ (Reducing Emissions from Deforestation and Forest Degradation) partnership works to promote the reduction of greenhouse gas (GHG) emissions by protecting forests in developing countries through positive incentives. It is regarded as an essential component of the post‐2012 climate regime to stabilize GHG emissions and engage developing countries in worldwide mitigation endeavours. This study focuses on the gap between agricultural revenue and REDD+ compensation through the construction of several scenarios that explore the impacts of possible carbon price ranges.Three scenarios that reflect different potential policies are examined: (1) current carbon trading; (2) carbon trading with all forestry activities; and (3) carbon trading with all countries participating gradually over the coming decades. Data for developing the scenarios were obtained through a case study in central Kalimantan, Indonesia, by interrogating the potential for revenue by expanding agricultural land. The results indicate that REDD+ payments could not effectively compensate land users for their opportunity cost of deforestation, making it difficult for the governments to ensure that REDD+ money “reaches the ground” in terms of balancing the agricultural revenue of land users.     

Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies1

Anderson, SallyRose, Glenn Tootle, and Henri Grissino‐Mayer, 2012. Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies. Journal of the American Water Resources Association (JAWRA) 48(4): 849‐858. DOI: 10.1111/j.1752‐1688.2012.00651.x Abstract: Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. We used tree‐ring chronologies to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k‐nearest neighbor techniques. We correlated moisture sensitive tree‐ring chronologies in and adjacent to the UCRB with regional soil moisture and tested the relationships for temporal stability. Chronologies that were positively correlated and stable for the calibration period were retained. We used stepwise linear regression to identify the best predictor combinations for each soil moisture region. The regressions explained 42‐78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained more variance in the datasets. Reconstructed soil moisture data was standardized and compared with standardized reconstructed streamflow and snow water equivalent data from the same region. Soil moisture and other hydrologic variables were highly correlated, indicating reconstructions of soil moisture in the UCRB using tree‐ring chronologies successfully represent hydrologic trends.