Correction to: New insights into the rainfall variability in the tropical Andes on seasonal and interannual time scales

Climate Dynamics - The original version of the article contained errors in Fig.     

Estimating urban above ground biomass with multi-scale LiDAR

Background

Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.

Results

Camden has an estimated median AGB density of 51.6 Mg ha^–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤?25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.

Conclusion

Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

     

An Automatic Well Planner for Complex Well Trajectories

Mathematical Geosciences - A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry....     

Derivation Of Refractive Index And Temperature Gradients From Optical Scintillometry To Correct Atmospherically Induced Errors For Highly Precise Geodetic Measurements

Refraction effects of optical beams are generally caused by an inhomogeneous propagation medium and are a major source of systematic errors in the precise optical determination of angles and distances in the atmospheric surface layer. In this contribution a method for deriving vertical temperature and refractive index gradients from optical scintillation is presented. Knowledge of these gradients is required for the compensation of atmospherically induced errors for highly precise terrestrial geodetic measurements, like direct transfer and levelling. The advantage of the present optical method is, that temperature and refractive index gradients can be derived as line-averaged values over the propagation path, which is not possible by meteorological point measurements. Field observations have been carried out with a displaced-beam scintillometer over flat terrain and under different atmospheric conditions in order to verify this method. The experiments show, that this method allows to derive accurate correction values for precise terrestrial geodetic measurements.     

Processing and quality control of flux data during LITFASS-2003

Different aspects of the quality assurance and quality control (QA/QC) of micrometeorological measurements were combined to create a comprehensive algorithm which was then applied to experimental data from LITFASS-2003 (Lindenberg Inhomogeneous Terrain—Fluxes between Atmosphere and Surface: a long term Study). Eddy-covariance measurements of the latent heat flux were the main focus of the QA/QC efforts. The results of a turbulence sensor intercomparison experiment showed deviations between the different eddy-covariance systems on the order of 15%, or less than 30 W m⁻², for the latent heat flux and 5%, or less than 10 W m⁻², for the sensible heat flux. In order to avoid uncertainties due to the post-processing of turbulence data, a comprehensive software package was used for the analysis of experimental data from LITFASS-2003, including all necessary procedures for corrections and quality control. An overview of the quality test results shows that for most of the days more than 80% of the available latent heat flux data are of high quality so long as there are no instrumental problems. The representativeness of a flux value for the target land-use type was analysed using a stochastic footprint model. Different methods to calculate soil heat fluxes at the surface are discussed and a sensitivity analysis is conducted to select the most robust method for LITFASS-2003. The lack of energy balance closure, which was found for LITFASS-2003, can probably be attributed to the presence of low-frequency flux contributions that cannot be resolved with an averaging time of 30 min. Though the QA/QC system has been developed for the requirements of LITFASS-2003, it can also be applied to other experiments dealing with similar objectives.     

The effect of mountainous topography on moisture exchange between the “surface” and the free atmosphere

Typical numerical weather and climate prediction models apply parameterizations to describe the subgrid-scale exchange of moisture, heat and momentum between the surface and the free atmosphere. To a large degree, the underlying assumptions are based on empirical knowledge obtained from measurements in the atmospheric boundary layer over flat and homogeneous topography. It is, however, still unclear what happens if the topography is complex and steep. Not only is the applicability of classical turbulence schemes questionable in principle over such terrain, but mountains additionally induce vertical fluxes on the meso-γ scale. Examples are thermally or mechanically driven valley winds, which are neither resolved nor parameterized by climate models but nevertheless contribute to vertical exchange. Attempts to quantify these processes and to evaluate their impact on climate simulations have so far been scarce. Here, results from a case study in the Riviera Valley in southern Switzerland are presented. In previous work, measurements from the MAP-Riviera field campaign have been used to evaluate and configure a high-resolution large-eddy simulation code (ARPS). This model is here applied with a horizontal grid spacing of 350 m to detect and quantify the relevant exchange processes between the valley atmosphere (i.e. the ground “surface” in a coarse model) and the free atmosphere aloft. As an example, vertical export of moisture is evaluated for three fair-weather summer days. The simulations show that moisture exchange with the free atmosphere is indeed no longer governed by turbulent motions alone. Other mechanisms become important, such as mass export due to topographic narrowing or the interaction of thermally driven cross-valley circulations. Under certain atmospheric conditions, these topographical-related mechanisms exceed the “classical” turbulent contributions a coarse model would see by several times. The study shows that conventional subgrid-scale parameterizations can indeed be far off from reality if applied over complex topography, and that large-eddy simulations could provide a helpful tool for their improvement.     

European floods during the winter 1783/1784: scenarios of an extreme event during the ‘Little Ice Age’

The Lakagígar eruption in Iceland during 1783 was followed by the severe winter of 1783/1784, which was characterised by low temperatures, frozen soils, ice-bound watercourses and high rates of snow accumulation across much of Europe. Sudden warming coupled with rainfall led to rapid snowmelt, resulting in a series of flooding phases across much of Europe. The first phase of flooding occurred in late December 1783–early January 1784 in England, France, the Low Countries and historical Hungary. The second phase at the turn of February–March 1784 was of greater extent, generated by the melting of an unusually large accumulation of snow and river ice, affecting catchments across France and Central Europe (where it is still considered as one of the most disastrous known floods), throughout the Danube catchment and in southeast Central Europe. The third and final phase of flooding occurred mainly in historical Hungary during late March and early April 1784. The different impacts and consequences of the above floods on both local and regional scales were reflected in the economic and societal responses, material damage and human losses. The winter of 1783/1784 can be considered as typical, if severe, for the Little Ice Age period across much of Europe.