Morten Andreas Dahl Larsen

Removal of cyanide by woody plants.

Hydrogen cyanide is a high volume production chemical that causes severe environmental problems. The toxicity of potassium cyanide (KCN) to basket willow trees (Salix viminalis) was tested. In aqueous solution, 2 mg CN l(-1) as KCN depressed the transpiration after 72 h about 50%. Trees exposed to 0.4 mg CN l(-1) in aqueous solution showed initially a depression of transpiration, but recovered. Doses of 8 and 20 mg CN l(-1) in aqueous solution were quickly mortal to the trees. At the end of the test, almost all cyanide had disappeared from the solutions. Levels of cyanide in plants were related to the toxicity, with no elevated levels of cyanide in plants exposed to 0.4 mg CN l(-1). Willows grown in sand survived 423.5 h irrigation with 20 mg CN l(-1). Willows grown in sand irrigated with 50 mg CN l(-1) died within a few days. The roots of the surviving willows were able to consume about 10 mg CN kg fresh weight(-1)h(-1). Vascular plants possess the enzymes beta-cyanoalanine synthase and beta-cyanoalanine hydrolase, which convert free cyanide to the amino acid asparagine. The in vivo capacity of woody plants (willow, poplar, elder, rose, birch) to remove cyanide was evaluated. Tests were performed with detached leaves and roots in KCN solutions of different concentrations. The highest removal capacity was obtained for basket willow hybrids (Salix viminalis x schwerinii). The Michaelis-Menten kinetics was determined. Realistic values of the half-saturation constant, K(M), were between 0.6 and 1.7 mg CN l(-1); the maximum metabolic capacity, v(max), was around 9.3 mg CN kg fresh weight(-1)h(-1). The removal of cyanide by plants might be useful in phytoremediation and treatment of wastewater from gold mining.

Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions

In this article, six individual tree crown (ITC) detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following (VF), region-growing (RG), template matching (TM), scale-space (SS) theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent tree crown detection, even by human interpreters.

On the role of domain size and resolution in the simulations with the HIRHAM region climate model

We investigate the simulated temperature and precipitation of the HIRHAM regional climate model using systematic variations in domain size, resolution and detailed location in a total of eight simulations. HIRHAM was forced by ERA-Interim boundary data and the simulations focused on higher resolutions in the range of 5.5–12xa0km. HIRHAM outputs of seasonal precipitation and temperature were assessed by calculating distributed model errors against a higher resolution data set covering Denmark and a 0.25° resolution data set covering Europe. Furthermore the simulations were statistically tested against the Danish data set using bootstrap statistics. The results from the distributed validation of precipitation showed lower errors for the winter (DJF) season compared to the spring (MAM), fall (SON) and, in particular, summer (JJA) seasons for both validation data sets. For temperature, the pattern was in the opposite direction, with the lowest errors occurring for the JJA season. These seasonal patterns between precipitation and temperature are seen in the bootstrap analysis. It also showed that using a 4,000xa0×xa02,800xa0km simulation with an 11xa0km resolution produced the highest significance levels. Also, the temperature errors were more highly significant than precipitation. In similarly sized domains, 12 of 16 combinations of variables, observation validation data and seasons showed better results for the highest resolution domain, but generally the most significant improvements were seen when varying the domain size.

Local control on precipitation in a fully coupled climate-hydrology model.

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies.

THE ROLE OF PLANTS AND BACTERIA IN PHYTOREMEDIATION - KINETIC ASPECTS

Phytoremediation is the common name for cleaning techniques for polluted soils, sediments, and wastewaters using plants. It has been shown repeatedly that several types of pollutants, e.g., petroleum products and solvents, are degraded faster in the presence of plants. A couple of processes are known to influence the elimination of pollutants, among them transpiration of water, oxygen transport, biological stimulation in the root zone and plant uptake of chemicals. However, it is frequently unclear whether the plants directly metabolise the pollutants, or whether they only play an indirect role by supporting microbial action. The metabolism kinetics of plant enzymes is mathematically described by the Michaelis-Menten kinetics. This means, that at low substrate concentrations, the degradation is first order, whereas it is linear and therefore limited at high substrate concentrations. Bacteria use the substrate for growth, and grow better at higher substrate availability. This is described by the Monod kinetics. Therefore, bacteria have a limited degradation capacity at low substrate concentrations. This often prohibits the biodegradation of polluted sites down to required levels. The combination of plants with bacteria might be a successful method to overcome these short-comings.

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACT This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models. Editor D. Koutsoyiannis; Associate editor not assigned

Assessing the influence of groundwater and land surface scheme in the modelling of land surface–atmosphere feedbacks over the FIFE area in Kansas, USA

The land surface–atmosphere interaction is described differently in large-scale surface schemes of regional climate models and small-scale spatially distributed hydrological models. In particular, the hydrological models include the influence of shallow groundwater on evapotranspiration during dry periods where soils are depleted and groundwater is the only water supply. These mechanisms are analysed by combining a distributed hydrological model (MIKE SHE) and a regional climate model (HIRHAM) and comparing simulation results to the FIFE area observation data in Kansas, USA. The numerical experiments include five simulations. First MIKE SHE is forced by observed climate data in two versions (1) with groundwater at a fixed uniform depth, and (2) with a dynamical groundwater component simulating shallow groundwater conditions in river valleys. (3) In a third simulation, MIKE SHE is forced by HIRHAM-simulated precipitation. The last two simulations include (4) a standard HIRHAM simulation, and (5) a fully coupled HIRHAM-MIKE SHE simulation locally replacing the land surface scheme by MIKE SHE for the FIFE area, while HIRHAM in standard configuration is used for the remaining model area. The results show a clear correlation between depth to the groundwater and evapotranspiration with a distinct groundwater depth threshold at 0.5–3xa0m. During the dry summer period, the two MIKE SHE simulations using distributed groundwater reproduced evapotranspiration better than MIKE SHE with unsaturated flow alone and the HIRHAM simulations. This indicates that including dynamic groundwater in a fully coupled climate-hydrology model may improve evapotranspiration fluxes from areas with shallow groundwater tables.

Branch and bound solution of the multidimensional assignment problem formulation of data association

The well-known index-based tree branch and bound algorithm for solution of the multidimensional assignment problem (MAP) is extended to handle multiple assignable 0-value indices, such as found in MAPs arising from the data association problem. Three different lower bounds for partial solutions are proposed and their effectiveness examined. Computational experiments are performed on instances of different size, different feasibility structure and sparseness, and different cost structure. It is shown how the use of a small range of integer weights commonly used in computational experiments on the solution of the MAP gives misleading results. The effects of different improvements of the basic algorithm are examined, including caching of feasibility tests, sorting of branches on each level of the tree, pruning of high cost branches, promotion of branches in the best known solution, and local search. Conclusions are that all of the proposed improvements reduce running times, especially sorting and pruning.

Simulation of Optimal Decision-Making Under the Impacts of Climate Change

Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

Water use in electricity generation for water-energy nexus analyses: The European case

With almost 40% of the global population suffering from water scarcity, the need to manage water resources is evidently urgent. While water and energy systems are intrinsically linked, the availability of comprehensive, integrated data sets across the domains of water and energy is generally lacking. As a result, estimated indicators representing volumes of water usage per unit of electricity or fuel produced are often required to analyse the water-energy nexus. In this paper, an ensemble of indicators is assembled representing water usage spanning different electricity-generation technologies based on previously published works in an attempt to depict the level or lack of detail in current large-scale energy-sector water-usage data. Based on these, the degree in which using such estimates is suitable for reproducing electricity-production water-usage at coarser spatio-temporal scales is assessed. The performance of the ensemble median/min/max as a predictor of water use is evaluated for the period from 1980 to 2015 using additional information about the constituents of the European energy system. Comparing with the reported values for 1980-2015, the median provides a skillful reproduction of historical yearly water use for the EU (EU28) as a whole. A further analysis for 2015 indicates that reasonable agreement is also seen at the country level. Thus, the results suggest that an ensemble-based approach has the potential to provide sturdy estimates of yearly water use by energy systems for analyses at both the country and regional levels.