Footprints in Homogeneously and Heterogeneously Driven Boundary Layers Derived from a Lagrangian Stochastic Particle Model Embedded into Large-Eddy Simulation

A Lagrangian stochastic (LS) model, which is embedded into a parallelised large-eddy simulation (LES) model, is used for dispersion and footprint evaluations. For the first time an online coupling between LES and LS models is applied. The new model reproduces concentration patterns, which were obtained in prior studies, provided that subgrid-scale turbulence is included in the LS model. Comparisons with prior studies show that the model evaluates footprints successfully. Streamwise dispersion leads to footprint maxima that are situated less far upstream than previously reported. Negative flux footprints are detected in the convective boundary layer (CBL). The wide range of applicability of the model is shown by applying it under neutral and stable stratification. It is pointed out that the turning of the wind direction with height leads to a considerable dependency of source areas on height. First results of an application to a heterogeneously heated CBL are presented, which emphasize that footprints are severely affected by the inhomogeneity.     

Update of a Footprint-Based Approach for the Characterisation of Complex Measurement Sites

Horizontal heterogeneity can significantly affect the flux data quality at monitoring sites in complex terrain. In heterogeneous conditions, the adoption of the eddy-covariance technique is contraindicated by the lack of horizontal homogeneity and presence of advective conditions. In addition, uncertainty concerning the sources or sinks influencing a measurement compromises the data interpretation. The consideration of the spatial context of a measurement, defined by a footprint analysis, can therefore provide an important tool for data quality assessment. This study presents an update of an existing footprint-based quality evaluation concept for flux measurement sites in complex terrain. The most significant modifications in the present version are the use of a forward Lagrangian stochastic trajectory model for the determination of the spatial context of the measurements, and the determination of effective roughness lengths with a flux aggregation model in a pre-processing step. Detailed terrain data gathered by remote sensing methods are included. This approach determines spatial structures in the quality of flux data for varying meteorological conditions. The results help to identify terrain influences affecting the quality of flux data, such as dominating obstacles upwind of the site, or slopes biasing the wind field, so that the most suitable footprint regions for the collection of high-quality datasets can be identified. Additionally, the approach can be used to evaluate the performance of a coordinate rotation procedure, and to check to what extent the measured fluxes are representative for a target land-use type.     

Climatology of precipitation extremes in Estonia using the method of moving precipitation totals

A method of moving precipitation totals is described and applied for the analysis of precipitation extremes in Estonia. Numbers of extremely wet and extremely dry days and other indices of precipitation extremes were calculated using the daily precipitation data measured at 51 stations over Estonia during 1957–2009. Mean regularities of spatial and seasonal distribution were determined. Long-term changes were detected using Sen's method and Mann–Kendall test. The highest risk of heavy precipitation is in the regions of higher mean precipitation on the uplands and on the belt of higher precipitation in the western part of continental Estonia. Wet spells have their sharp maxima in July and August. The highest risk of droughts is observed in the coastal regions of West Estonia. In the coastal area, droughts appear mostly in the first half of summer, while in the eastern Estonia, they are usually observed during the second half of summer. Extreme precipitation events have become more frequent and intense. Statistically significant increasing trends were, first of all, found in the time series of winter extreme precipitation indices. In summer and autumn, trends existed in some indices, but in spring, there were no trends at all. There were no trends in time series of dryness indices in Estonia in 1957–2009.     

A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland

Knowledge of oil-induced impacts from the literature and experts were used to develop a Bayesian network to evaluate the biological consequences of an oil accident in the low-saline Gulf of Finland (GOF). Analysis was carried out for selected groups of organisms. Subnetworks were divided into subgroups according to a predicted response to oil exposure. Two scenario analyses are presented: the most probable and the worst-case accident.The impact of the most probable accident in the GOF is rather small. In most of the groups studied oil-induced long-term effects are evaluated to be minor at least from the perspective of the whole GOF. After the worst-case accident negative effects are more likely. The model predicts that the most vulnerable groups are auks and ducks. Amphipods, gulls and to a lesser extend littoral fishes and seals may show delayed recovery after an accident. Also annual plant species may be susceptible to oil-induced disturbances.     

Footprints and Fetches for Fluxes over Forest Canopies with Varying Structure and Density

A stochastic trajectory model was used to estimate scalar fluxfootprints in neutral stabilityfor canopies of varying leaf area distributions andleaf area indices. An analytical second-order closure model wasused to predict mean wind speed, second moments and the dissipationrate of turbulent kinetic energy within a forest canopy.The influence of source vertical profile on the flux footprint wasexamined. The fetch is longer for surface sourcesthan for sources at higher levels in the canopy. In order tomeasure all the flux components, and thus the total flux, with adesired accuracy, sources were located at the forest floor in thefootprint function estimation. The footprint functions werecalculated for five observation levels above the canopy top. Itwas found that at low observation heights both canopy density andcanopy structure affect the fetch. The higher abovethe canopy top the flux is measured, the more pronounced is the effectof the canopy structure. The forest fetch for flux measurements isstrongly dependent on the required accuracy: The 90% flux fetchis greater by a factor of two or more compared to the 75% fetch. Theupwind distance contributing 75% of flux is as large as 45 timesthe difference between canopy height and the observation heightabove the canopy top, being even larger for low observationlevels.     

Primary production, sedimentation and resuspension in large shallow Lake Võrtsjärv

: Phytoplankton primary production measurements and sediment trapping in the large (270 km²) shallow (mean depth 2.8 m) Lake Võrtsjärv were performed during ice-free periods in 1995-1996. Sedimentation rate varied from 26 to 700 g DWm^-2d^-1 (DW=dry weight), with a mean value of 170 g DWm^-2d^-1. Under the influence of prevailing westerly and south-westerly winds, the sedimentation rate was higher in the northern part, and lower in the western and southern parts of the lake. An increasing trend towards autumn could be followed in both years. The mean sedimentation rates in the dry year 1996 with a low lake water level exceeded those of the “normal” 1995 by a factor of more than two. New sedimentation formed 10-15 % of the downward sediment flux in 1995 and 4-6 % in 1996, while the major part of entrapped matter originated from resuspended bottom sediments. The dynamics of gross sedimentation showed no relation to the primary production but correlated significantly with the concentration of suspended solids, with the average wind speed and with the mean depth of the lake during trap exposition.¶The composition of entrapped matter did not show seasonal variation but differed between the sampling stations. The amount of particulate organic matter (POM) per DW varied from 11 % at places where the bottom sediment consisted of moraine or sand to 28 % at places with muddy bottom. N:POM was rather stable (45-61 mgg^-1, Cvar = 6 %). The variability of P:POM (3.5-7.1, Cvar = 20 %) exceeded that of P:DW (0.75-1.31 mgg^-1, Cvar = 10 %) showing the essential role of mineral-bound P in the settling material. In these conditions, the N:P ratio was determined by the POM content of settling matter. POM:DW in settling material was generally higher than in the 0-2 cm layer of bottom sediments.     

Evaluation of trends and predictability of short‐term droughts in three meteorological subdivisions of India using multivariate EMD‐based hybrid modelling

This paper presented trend analysis of droughts in Kerala, Telangana, and Orissa meteorological subdivisions in India and proposed a framework for drought prediction by employing the Empirical Mode Decomposition (EMD)‐based prediction models. The study used 3‐month standardized precipitation index (SPI3) for drought analysis. The trend analysis of SPI3 series for the period 1871–2012 using Mann–Kendall method showed statistically significant increasing trend in Kerala and Telangana subdivisions and a decreasing trend in Orissa subdivision. In addition, the non‐linear trend component extracted from EMD showed statistically significant changes in all the three subdivisions. Then, the study proposed a hybrid approach for prediction of short‐term droughts by coupling multivariate extension of EMD (MEMD) with stepwise linear regression (SLR) and genetic programming (GP) methods. First, the multivariate dataset comprising the SPI3 series of current and lagged time steps are decomposed using the MEMD. Then, SLR/GP models are developed to predict each subseries of SPI3 of desired time step considering the subseries of predictor variables at the corresponding timescales as inputs. The resulting models at different timescales are recombined to obtain the SPI3 values of the desired time step. The method is applied for prediction of short‐term droughts in the three subdivisions. The results obtained by the hybrid models are compared with that obtained by conventional prediction models using M5 Model Trees and GP. The rigorous performance evaluation based on multiple statistical criteria clearly exhibited the superiority of the hybrid approaches (i.e., prediction models with MEMD‐based decomposition over models without decomposition) for prediction of SPI3 in three subdivisions. Further, the study found that MEMD‐GP model performs marginally better than the MEMD‐SLR model due to its efficacy in modelling high frequency modes.