Amazonian soil fungi are efficient degraders of glyphosate herbicide; novel isolates of Penicillium, Aspergillus, and Trichoderma

Pesticide residues that contaminate the environment circulate within the hydrological cycle can accumulate within the food chain and cause problems to both environmental and human health. Microbes, however, are well known for their metabolic versatility and the ability to degrade chemically stable substances, including recalcitrant xenobiotics. The current study focused on bio-prospecting within Amazonian rainforest soils to find novel strains fungi capable of efficiently degrading the agriculturally and environmentally ubiquitous herbicide, glyphosate. Of 50 fungal strains isolated (using culture media supplemented with glyphosate as the sole carbon-substrate), the majority were Penicillium strains (60%) and the others were Aspergillus and Trichoderma strains (26 and 8%, respectively). All 50 fungal isolates could use glyphosate as a phosphorous source. Eight of these isolates grew better on glyphosate-supplemented media than on regular Czapek Dox medium. LC-MS revealed that glyphosate degradation by Penicillium 4A21 resulted in sarcosine and aminomethylphosphonic acid.

Impacts of Climatic Variation and Human Activity on Runoff in Western China

Hydrological cycle is sensitively affected by climatic variation and human activity. Taking the upper- and middle-stream of the Weihe River in western China as an example, using multiple meteorological and hydrological elements, as well as land-use/land-cover change (LUCC) data, we constructed a sensitivity model of runoff to climatic elements and human activities based on the hydro-thermal coupling equilibrium equation, while a cumulative slope was used to establish a comprehensive estimation model for the contributions of climatic variation and human activities to the changes of runoff. The results showed that the above function model established could be well applied to quantitatively study the elasticity of runoff’s response to climatic variation and human activities. It was found that the annual average precipitation, evaporation, wind velocity, sunshine hours, relative humidity and runoff showed decreasing trends and that temperature increased. While in the hydrological cycle, precipitation and relative humidity had a non-linear positive driving effect on runoff, while temperature, evaporation, sunshine hours, wind velocity, and land-use/land-cover change (LUCC) have non-linearly negatively driven the variation of runoff. Moreover, runoff has a strong sensitive response to precipitation, evaporation and LUCC. In areas with strong human activities, the sensitivity of runoff to climatic change was decreasing, and runoff has a greater elastic response to underlying surface parameters. In addition, the analysis showed that the abrupt years of climate and runoff changes in the Weihe River Basin were 1970, 1985 and 1993. Before 1985, the contribution rate of climatic variation to runoff was 68.3%, being greater than that of human activities to runoff, and then the contribution rates of human activities to runoff reached 75.1%. The impact of natural climate on runoff was weakened, and the effect of human activities on runoff reduction increased. Under 30 hypothetical climatic scenarios, the evaluation of runoff in the future showed that the runoff in the Weihe River Basin will be greatly reduced, and the reduction will be more significant during the flood season. Comparing the geographically fragile environments and intense human activities, it was believed that climatic variation had a dramatic effect on driving the water cycle of precipitation and evaporation and affected regional water balance and water distribution, while human activities had driven the hydrological processes of the underlying surface, thus becoming the main factors in the reduction of runoff. This study provided scientific tools for regional climate change and water resources assessment.

Acquisition of Rainfall in Ungauged Basins: a Study of Rainfall Distribution Heterogeneity Based on a New Method

High-density precipitation data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involves high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the Proper Orthogonal Decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall data in ungauged basins. (2) Compared with the commonly used Thiessen Polygon method, the Inverse Distance Weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Hunting for Information in Streamflow Signatures to Improve Modelled Drainage

About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km2) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.

Bioaerosols and atmospheric ice nuclei in a Mediterranean dryland: community changes related to rainfall

Certain biological particles are highly efficient ice nuclei (IN), but the actual contribution of bioparticles to the pool of atmospheric IN and their relation to precipitation are not well characterized. We investigated the composition of bioaerosols, ice nucleation activity, and the effect of rainfall by metagenomic sequencing and freezing experiments of aerosol samples collected during the INUIT 2016 campaign in a rural dryland on the eastern Mediterranean island of Cyprus. Taxonomic analysis showed community changes related to rainfall. For the rain-affected samples, we found higher read proportions of fungi, particularly of Agaricomycetes, which are a class of fungi that actively discharge their spores into the atmosphere in response to humidity changes. In contrast, the read proportions of bacteria were reduced, indicating an effective removal of bacteria by precipitation. Freezing experiments showed that the IN population in the investigated samples was influenced by both rainfall and dust events. For example, filtration and heat treatment of the samples collected during and immediately after rainfall yielded enhanced fractions of heat-sensitive IN in the size ranges larger than 5 µm and smaller than 0.1 µm, which were likely of biological origin (entire bioparticles and soluble macromolecular bio-IN). In contrast, samples collected in periods with dust events were dominated by heat-resistant IN active at lower temperatures, most likely mineral dust. The DNA analysis revealed low numbers of reads related to microorganisms that are known to be IN-active. This may reflect unknown sources of atmospheric bio-IN as well as the presence of cell-free IN macromolecules that do not contain DNA, in particular for sizes < 0.1 µm. The observed effects of rainfall on the composition of atmospheric bioaerosols and IN may influence the hydrological cycle (bioprecipitation cycle) as well as the health effects of air particulate matter (pathogens, allergens).

Enhanced Symbiotic Organisms Search and Hydrological Cycle Algorithms for Real Power Loss Diminution and Voltage Stability Enhancement

In this chapter, enhanced symbiotic organisms search (ESOS) algorithm and hydrological cycle (HC) algorithm are projected to solve factual power loss lessening problem. Symbiotic search algorithm is based on the actions between two different organisms in the ecosystem: mutualism, commensalism, and parasitism. Exploration procedure has been initiated arbitrarily, and each organism indicates a solution with fitness value. Quasi-oppositional-based learning and chaotic local search have been applied to augment the performance of the algorithm. In this work, hydrological cycle (HC) algorithm has been utilized to solve the optimal reactive power problem. It imitates the circulation of water form land to sky and vice versa. Only definite number of water droplets is chosen for evaporation, and it is done through roulette-wheel selection method. In the condensation stage, water drops move closer, combine, and also collusion occurs as the temperature decreases.