On the variability of the Charnock constant and the functional dependence of the drag coefficient on wind speed

A model for the air–sea interface, based on the coupled pair of similarity relations for “aerodynamically” rough flow in both fluids, is presented, which is applied to fetch-limited and high wind speed conditions which occur, for example, in hurricanes. It is shown that the specification of the maximum 10-m drag coefficient and the 10-m wind speed and the peak wave speed at which it occurs are sufficient to uniquely determine the drag law, which asymptotes at low wind speeds to a Charnock constant similar to that for the fully developed wind wave sea and is almost independent of the peak wave speed at the maximum in drag coefficient. A feature of the drag law is that it is of Charnock form, almost independent of the wave age, consistent with the transfer of momentum to the wave spectrum being due to the smaller rather than the dominant wavelengths. The analysis is also applied to a variable sea state in which either the surface wind or the surface Stokes drift vary, but the peak wave speed is kept constant. The corresponding variability in the Charnock constant is in general accord with observations.     

An Attempt to Partition Stomatal and Non-stomatal Ozone Deposition Parts on a Short Grassland

To evaluate the damaging effect of tropospheric ozone on vegetation, it is important to evaluate the stomatal uptake of ozone. Although the stomatal flux is a dominant pathway of ozone deposition onto vegetated surfaces, non-stomatal uptake mechanisms such as soil and cuticular deposition also play a vital role, especially when the leaf area index \({LAI}< 4\). In this study, we partitioned the canopy conductance into stomatal and non-stomatal components. To calculate the stomatal conductance of water vapour for sparse vegetation, we firstly partitioned the latent heat flux into effects of transpiration and evaporation using the Shuttleworth–Wallace (SW) model. We then derived the stomatal conductance of ozone using the Penman–Monteith (PM) theory based on the similarity to water vapour conductance. The non-stomatal conductance was calculated by subtracting the stomatal conductance from the canopy conductance derived from directly-measured fluxes. Our results show that for short vegetation (LAI \(=\) 0.25) dry deposition of ozone was dominated by the non-stomatal flux, which exceeded the stomatal flux even during the daytime. At night the stomatal uptake of ozone was found to be negligibly small. In the case of vegetation with \({LAI}\approx 1\), the daytime stomatal and non-stomatal fluxes were of the same order of magnitude. These results emphasize that non-stomatal processes must be considered even in the case of well-developed vegetation where cuticular uptake is comparable in magnitude with stomatal uptake, and especially in the case of vegetated surfaces with \({LAI}< 4\) where soil uptake also has a role in ozone deposition.     

Comparative study of different wavelets for developing parsimonious Volterra model for rainfall-runoff simulation

Although the Volterra models are non-parsimonious ones, they are being used because they can mimic dynamics of complex systems. However, applying and identification of the Volterra models using data may result in overfitting problem and uncertainty. In this investigation we evaluate capability of different wavelet forms for decomposing and compressing the Volterra kernels in order to overcome this problem by reducing the number of the model coefficients to be estimated and generating smooth kernels. A simulation study on a rainfall?runoff process over the Cache River watershed showed that the method performance is successful due to multi-resolution capacity of the wavelet analysis and high capability of the Volterra model. The results also revealed that db2 and sym2 wavelets have the same high potential in improving the linear Volterra model performance. However, QS wavelet was more successful in yielding smooth kernels. Moreover, the probability of overfitting while identifying the nonlinear Volterra model may be less than the linear model.     

Association of proportions

Simple correlation leads to misleading results when applied to the proportions of materials in mixtures, because of the constraint that the total of the proportions is less than one. A simple mainly graphical procedure to determine whether the proportions in mixtures are associated after allowing for the constraint is presented. No assumptions about the form of the underlying probability distributions are required. The technique is extended to problems where other constraints apply.     

Gap Filling and Quality Assessment of CO2 and Water Vapour Fluxes above an Urban Area with Radial Basis Function Neural Networks

Vertical turbulent fluxes of water vapour, carbon dioxide, and sensible heat were measured from 16 August to the 28 September 2006 near the city centre of Münster in north-west Germany. In comparison to results of measurements above homogeneous ecosystem sites, the CO₂ fluxes above the urban investigation area showed more peaks and higher variances during the course of a day, probably caused by traffic and other varying, anthropogenic sources. The main goal of this study is the introduction and establishment of a new gap filling procedure using radial basis function (RBF) neural networks, which is also applicable under complex environmental conditions. We applied adapted RBF neural networks within a combined modular expert system of neural networks as an innovative approach to fill data gaps in micrometeorological flux time series. We found that RBF networks are superior to multi-layer perceptron (MLP) neural networks in the reproduction of the highly variable turbulent fluxes. In addition, we enhanced the methodology in the field of quality assessment for eddy covariance data. An RBF neural network mapping system was used to identify conditions of a turbulence regime that allows reliable quantification of turbulent fluxes through finding an acceptable minimum of the friction velocity. For the data analysed in this study, the minimum acceptable friction velocity was found to be 0.15 m s⁻¹. The obtained CO₂ fluxes, measured on a tower at 65 m a.g.l., reached average values of 12 μmol m⁻² s⁻¹ and fell to nighttime minimum values of 3 μmol m ⁻² s⁻¹. Mean daily CO₂ emissions of 21 g CO₂ m⁻²d ⁻¹ were obtained during our 6-week experiment. Hence, the city centre of Münster appeared to be a significant source of CO₂. The half-hourly average values of water vapour fluxes ranged between 0.062 and 0.989 mmol m⁻² s⁻¹and showed lower variances than the simultaneously measured fluxes of CO₂.     

Patuxent Landscape Model. III. Model calibration

Using the LHEM/SME the Patuxent Landscape Model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. The model addresses the effects of both the magnitude and spatial patterns of land use change and agricultural practices on hydrology, plant productivity, and nutrient cycling in the landscape. The spatial resolution for the full Patuxent watershed is 1 km², while subwatersheds are analyzed at a 200 × 200 m resolution to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model. We used a modular, multiscale approach to calibrate and test the model. Model results show good agreement with data.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.