Energy exchange in a dense urban environment Part II: impact of spatial heterogeneity of the surface

The centre of cities, characterised by spatial and temporal complexity, are challenging environments for micrometeorological research. This paper considers the impact of sensor location and heterogeneity of the urban surface on flux observations in the dense city centre of London, UK. Data gathered at two sites in close vicinity, but with different measurement heights, were analysed to investigate the influence of source area characteristics on long-term radiation and turbulent heat fluxes. Combining consideration of diffuse radiation and effects of specular reflections, the non-Lambertian urban surface is found to impact the measurements of surface albedo. Comparisons of observations from the two sites reveal that turbulent heat fluxes are similar under some flow conditions. However, they mostly observe processes at different scales due to their differing measurement heights, highlighting the critical impact of siting sensors in urban areas. A detailed source area analysis is presented to investigate the surface controls influencing the energy exchanges at the different scales

Attribution of the spatial pattern of CO2-forced sea level change to ocean surface flux changes

Climate models taking part in the coupled model intercomparison project phase 5 (CMIP5) all predict a global mean sea level rise for the 21st century. Yet the sea level change is not spatially uniform and differs among models. Here we evaluate the role of air–sea fluxes of heat, water and momentum (windstress) to find the spatial pattern associated to each of them as well as the spread they can account for. Using one AOGCM to which we apply the surface flux changes from other AOGCMs, we show that the heat flux and windstress changes dominate both the pattern and the spread, but taking the freshwater flux into account as well yields a sea level change pattern in better agreement with the CMIP5 ensemble mean. Differences among the CMIP5 control ocean temperature fields have a smaller impact on the sea level change pattern.

Modeling plant transpiration under limited soil water: comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models

Accurate estimates of how soil water stress affects plant transpiration are crucial for reliable land surface model (LSM) predictions. Current LSMs generally use a water stress factor, β, dependent on soil moisture content, θ, that ranges linearly between β = 1 for unstressed vegetation and β = 0 when wilting point is reached. This paper explores the feasibility of replacing the current approach with equations that use soil water potential as their independent variable, or with a set of equations that involve hydraulic and chemical signaling, thereby ensuring feedbacks between the entire soil–root–xylem–leaf system. A comparison with the original linear θ-based water stress parameterization, and with its improved curvi-linear version, was conducted. Assessment of model suitability was focused on their ability to simulate the correct (as derived from experimental data) curve shape of relative transpiration versus fraction of transpirable soil water. We used model sensitivity analyses under progressive soil drying conditions, employing two commonly used approaches to calculate water retention and hydraulic conductivity curves. Furthermore, for each of these hydraulic parameterizations we used two different parameter sets, for 3 soil texture types; a total of 12 soil hydraulic permutations. Results showed that the resulting transpiration reduction functions (TRFs) varied considerably among the models. The fact that soil hydraulic conductivity played a major role in the model that involved hydraulic and chemical signaling led to unrealistic values of β, and hence TRF, for many soil hydraulic parameter sets. However, this model is much better equipped to simulate the behavior of different plant species. Based on these findings, we only recommend implementation of this approach into LSMs if great care with choice of soil hydraulic parameters is taken

Soil carbon and litter development along a reconstructed biodiverse forest chronosequence of South-Western Australia

Soil organic matter (SOM) increases with time as landscape is restored. Studying SOM development along restored forest chronosequences would be useful in clarifying some of the uncertainties in quantifying C turnover rates with respect to forest clearance and ensuing restoration. The development of soil organic matter in the mineral soils was studied at four depths in a 16-year-old restored jarrah forest chronosequence. The size-separated SOM fractionation along with δ13C isotopic shift was utilised to resolve the soil C temporal and spatial changes with developing vegetation. The restored forest chronosequence revealed several important insights into how soil C is developing with age. Litter accumulation outpaced the native forest levels in 12 years after restoration. The surface soils, in general, showed increase in total C with age, but this trend was not clearly observed at lower depths. C accumulation was observed with increasing restoration age in all three SOM size-fractions in the surface 0–2 cm depth. These biodiverse forests show a trend towards accumulating C in recalcitrant stable forms, but only in the surface 0–2 cm mineral soil. A significant reverse trend was observed for the moderately labile SOM fraction for lower depths with increasing restoration age. Correlating the soil δ13C with total C concentration revealed the re-establishment of the isotopically depleted labile to enriched refractory C continuum with soil depth for the older restored sites. This implied that from a pedogenic perspective, the restored soils are developing towards the original native soil carbon profile.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Using Distributed Temperature Sensing to monitor field scale dynamics of ground surface temperature and related substrate heat flux

We present one of the first studies of the use of Distributed Temperature Sensing (DTS) along fibre-optic cables to purposely monitor spatial and temporal variations in ground surface temperature (GST) and soil temperature, and provide an estimate of the heat flux at the base of the canopy layer and in the soil. Our field site was at a groundwater-fed wet meadow in the Netherlands covered by a canopy layer (between 0-0.5 m thickness) consisting of grass and sedges. At this site, we ran a single cable across the surface in parallel 40 m sections spaced by 2 m, to create a 40×40 m monitoring field for GST. We also buried a short length (≈10 m) of cable to depth of 0.1±0.02 m to measure soil temperature. We monitored the temperature along the entire cable continuously over a two-day period and captured the diurnal course of GST, and how it was affected by rainfall and canopy structure. The diurnal GST range, as observed by the DTS system, varied between 20.94 and 35.08◦C; precipitation events acted to suppress the range of GST. The spatial distribution of GST correlated with canopy vegetation height during both day and night. Using estimates of thermal inertia, combined with a harmonic analysis of GST and soil temperature, substrate and soil-heat fluxes were determined. Our observations demonstrate how the use of DTS shows great promise in better characterising area-average substrate/soil heat flux, their spatiotemporal variability, and how this variability is affected by canopy structure. The DTS system is able to provide a much richer data set than could be obtained from point temperature sensors. Furthermore, substrate heat fluxes derived from GST measurements may be able to provide improved closure of the land surface energy balance in micrometeorological field studies. This will enhance our understanding of how hydrometeorological processes interact with near-surface heat fluxes.

Temporal and spatial variability of surface fluxes over the ice edge zone in the northern Baltic Sea

Three land-fast ice stations (one of them was the Finnish research ice breaker Aranda) and the German research aircraft Falcon were applied to measure the turbulent and radiation fluxes over the ice edge zone in the northern Baltic Sea during the Baltic Air-Sea-Ice Study (BASIS) field experiment from 16 February to 6 March 1998. The temporal and spatial variability of the surface fluxes is discussed. Synoptic weather systems passed the experimental area in a rapid sequence and dominated the conditions (wind speed, airsurface temperature difference, cloud field) for the variability of the turbulent and radiation fluxes. At the ice stations, the largest upward sensible heat fluxes of about 100 Wm�2 were measured during the passage of a cold front when the air cooled faster (�5 K per hour) than the surface. The largest downward flux of about �200 Wm�2 occurred during warm air advection when the air temperature reached +10�C but the surface temperature remained at 0�C. Spatial variability of fluxes was observed from the small scale (scale of ice floes and open water spots) to the mesoscale (width of the ice edge zone). The degree of spatial variability depends on the synoptic situation: during melting conditions downward heat fluxes were the same over ice and open water, whereas during strong cold-air advection upward heat fluxes differed by more than 100 Wm�2. A remarkable amount of grey ice with intermediate surface temperature was observed. The ice in the Baltic Sea cannot be described by one ice type only.

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII.

Spatial variability of soil CO2 emission in a sugarcane area characterized by secondary information

Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas.

Mineralogy of the clay fraction of Alfisols in two slope curvatures: III - spatial variability

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mineralogy of the clay fraction of alfisols in two slope curvatures: IV - spatial correlation with physical properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial variability of soil CO2 emission in different topographic positions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

GPS receivers for georeferencing of spatial variability of soil attributes

A caracterização da variabilidade espacial dos atributos do solo é indispensável para subsidiar práticas agrícolas de maneira sustentável. A utilização da geoestatística para caracterizar a variabilidade espacial desses atributos, como a resistência mecânica do solo à penetração (RP) e a umidade gravimétrica do solo (UG), é, hoje, prática usual na agricultura de precisão. O resultado da análise geoestatística é dependente da densidade amostral e de outros fatores, como o método de georreferencimento utilizado. Desta forma, o presente trabalho teve como objetivo comparar dois métodos de georreferenciamento para a caracterização da variabilidade espacial da RP e da UG, bem como a correlação espacial dessas variáveis. Foi implantada uma malha amostral de 60 pontos, espaçados em 20 m. Para as medições da RP, utilizou-se de penetrógrafo eletrônico e, para a determinação da UG, utilizou-se de trado holandês (profundidade de 0,0-0,1 m). As amostras foram georreferenciadas, utilizando-se do método de Posicionamento por Ponto Simples (PPS), com de (retirar) receptor GPS de navegação, e Posicionamento Relativo Semicinemático, com receptor GPS geodésico L1. Os resultados indicaram que o georreferenciamento realizado pelo PPS não interferiu na caracterização da variabilidade espacial da RP e da UG, assim como na estrutura espacial da relação dos atributos.

Small relief shape variations influence spatial variability of soil chemical attributes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)