Comparison of large aperture scintillometer and eddy covariance measurements: Can thermal infrared data be used to capture footprint-induced differences?

Eddy covariance (EC) and large aperture scintillometer (LAS) measurements were collected over an irrigated olive orchard near Marrakech, Morocco. The tall, sparse vegetation in the experimental site was relatively homogeneous, but during irrigation events spatial variability in soil humidity was large. This heterogeneity caused large differences between the source area characteristics of the EC system and the LAS, resulting in a large scatter when comparing sensible heat fluxes obtained from LAS and EC. Radiative surface temperatures were retrieved from thermal infrared satellite images from the Landsat Enhanced Thematical Mapper and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellites. Using these images in combination with an analytical footprint model, footprint-weighted radiative surface temperatures for the footprints of the LAS and the EC system were calculated. Comparisons between the difference in measured sensible heat fluxes and the difference in footprint-weighted radiative surface temperature showed that for differences between the footprint-weighted radiative surface temperatures larger than 0.5 K, correlations with the difference in measured sensible heat flux were good. It was found that radiative surface temperatures, obtained from thermal infrared satellite imagery, can provide a good indication of the spatial variability of soil humidity, and can be used to identify differences between LAS and EC measurements of sensible heat fluxes resulting from this variability

The use of the scintillation technique for monitoring seasonal water consumption of olive orchards in a semi-arid region

To monitor seasonal water consumption of agricultural fields at large scale, spatially averaged surface fluxes of sensible heat (H) and latent heat (LvE) are required. The scintillation method is shown to be a promising device for obtaining the area-averaged sensible heat fluxes, on a scale of up to 10 km. These fluxes, when combined with a simple available energy model, can be used to derive area-averaged latent heat fluxes. For this purpose, a Large Aperture Scintillometer (LAS) was operated continuously for more than one year over a tall and sparse irrigated oliveyard located in south-central Marrakesh (Morocco). Due to the flood irrigation method used in the site, which induces irregular pattern of soil moisture both in space and time, the comparison between scintillometer-based estimates of daily sensible heat flux (HLAS) and those measured by the classical eddy covariance (EC) method (HEC) showed a large scatter during the irrigation events, while a good correspondence was found during homogenous conditions (dry conditions and days following the rain events). We found, that combining a simple available energy model and the LAS measurements, the latent heat can be reliably predicted at large scale in spite of the large scatter (R2 = 0.72 and RMSE = 18.25Wm2) that is obtained when comparing the LAS against the EC. This scatter is explained by different factors: the difference in terms of the source areas of the LAS and EC, the closure failure of the energy balance of the EC, and the error in available energy estimates. Additionally, the irrigation efficiency was investigated by comparing measured seasonal evapotranspiration values to those recommended by the FAO. It was found that the visual observation of the physical conditions of the plant is not sufficient to efficiently manage the irrigation, a large quantity of water is lost (37% of total irrigation). Consequently, the LAS can be considered as a potentially useful tool to monitor the water consumption in complex conditions

Distinct genomic signals of lifespan and life history evolution in response to postponed reproduction and larval diet in Drosophila

Reproduction and diet are two major factors controlling the physiology of aging and life history, but how they interact to affect the evolution of longevity is unknown. Moreover, although studies of large‐effect mutants suggest an important role of nutrient sensing pathways in regulating aging, the genetic basis of evolutionary changes in lifespan remains poorly understood. To address these questions, we analyzed the genomes of experimentally evolved Drosophila melanogaster populations subjected to a factorial combination of two selection regimes: reproductive age (early versus postponed), and diet during the larval stage (“low,” “control,” “high”), resulting in six treatment combinations with four replicate populations each. Selection on reproductive age consistently affected lifespan, with flies from the postponed reproduction regime having evolved a longer lifespan. In contrast, larval diet affected lifespan only in early‐reproducing populations: flies adapted to the “low” diet lived longer than those adapted to control diet. Here, we find genomic evidence for strong independent evolutionary responses to either selection regime, as well as loci that diverged in response to both regimes, thus representing genomic interactions between the two. Overall, we find that the genomic basis of longevity is largely independent of dietary adaptation. Differentiated loci were not enriched for “canonical” longevity genes, suggesting that naturally occurring genic targets of selection for longevity differ qualitatively from variants found in mutant screens. Comparing our candidate loci to those from other “evolve and resequence” studies of longevity demonstrated significant overlap among independent experiments. This suggests that the evolution of longevity, despite its presumed complex and polygenic nature, might be to some extent convergent and predictable

Agrometerological study of semi-arid areas : an experiment for analysing the potential of time series of FORMOSAT-2 images (Tensift-Marrakech plain)

Earth Observing Systems designed to provide both high spatial resolution (10m) and high capacity of time revisit (a few days) offer strong opportunities for the management of agricultural water resources. The FORMOSAT-2 satellite is the first and only satellite with the ability to provide daily high-resolution images over a particular area with constant viewing angles. As part of the SudMed project, one of the first time series of FORMOSAT-2 images has been acquired over the semi-arid Tensift-Marrakech plain. Along with these acquisitions, an experimental data set has been collected to monitor land-cover/land-use, soil characteristics, vegetation dynamics and surface fluxes. This paper presents a first analysis of the potential of these data for agrometerological study of semi-arid areas

A Conceptual Flash Flood Early Warning System for Africa, Based on Terrestrial Microwave Links and Flash Flood Guidance

A conceptual flash flood early warning system for developing countries is described. The system uses rainfall intensity data from terrestrial microwave communication links and the geostationary Meteosat Second Generation satellite, i.e., two systems that are already in place and operational. Flash flood early warnings are based on a combination of the Flash Flood Guidance method and a hydrological model. The system will be maintained and operated through a public-private partnership, which includes a mobile telephone operator, a national meteorological service and an emergency relief service. The mobile telephone operator acts as both the supplier of raw input data and the disseminator of early warnings. The early warning system could significantly reduce the number of fatalities due to flash floods, improve the efficiency of disaster risk reduction efforts and play an important role in strengthening the resilience to climate change of developing countries in Africa. This paper describes the system that is currently being developed for Kenya

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05°) rainfall data for 15 years (1998–2012) over East Africa. The products’ assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes

On the application of scintillometry over heterogeneous grids

Summary In this paper the applicability of the Monin–Obukhov similarity theory (MOST) over heterogeneous terrain below the blending height is investigated. This is tested using two large aperture scintillometers (LAS), in conjunction with aggregation schemes to infer area-averaged refractive index structure parameters. The two LAS were operated simultaneously over the oliveyard of Agdal, located near Marrakech (Morocco). The Agdal olive yard is made up of two contrasted fields, or patches. The two sites are relatively homogeneous, but differ strongly in characteristics (mainly soil moisture status, and, to a lesser extent, vegetation cover). The higher soil moisture in the northern site creates heterogeneity at the scale of the entire olive yard (i.e. at grid scale). At patch scale, despite the complexity of the surface (tall, sparse trees), a good agreement was found between the sensible heat fluxes obtained from eddy-covariance systems and those estimated from the LAS. At grid scale, the aggregated structure parameter of the refractive index, simulated using the proposed aggregation model, behaves according to MOST. This aggregated structure parameter of the refractive index is obtained from measurements made below the grid scale blending height, and shows that MOST applies here. Consequently, scintillometers can be used at levels below the blending height. This is of interest, since strictly respecting the height requirements poses tremendous practical problems, especially if one is aiming to derive surface fluxes over large areas

Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies

Advances in remote sensing have led to the use of satellite-derived rainfall products to complement the sparse rain gauge data. Although these products are globally and some regional bias corrected, they often show substantial differences relative to ground measurements attributed to local and external factors that require systematic consideration. A decreasing rain gauge network inhibits the continuous validation of these products. Our proposal to deal with this problem was to use a Bayesian approach to merge the existing historical rain gauge information to create consistent satellite rainfall data for long-term applications. Monthly bias correction was applied to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS v2) using a corresponding gridded (0.05°) rain gauge data over East Africa for 33 years (1981–2013). The first 22 years were utilized to derive error fields which were then applied to independent CHIRPS data for 11 years for validation. Assessments of the approach’s influence on the rainfall estimates spatially and temporally were explored. Results showed a significant spatial reduction of the underestimation and overestimation of systematic errors at both monthly and yearly scales. The reduced errors increased with increased rainfall amounts, hence was less so in the relatively drier months. The overall monthly reduction of Root Mean Square Difference (RMSD) was between 4% and 60%, and the Mean Absolute Error (MAE) was between 1% and 63%, while the correlations improved by up to 21%. Yearly, the RMSD was reduced between 17% and 49%, and the MAE between 13% and 48%, while the increase in correlations was between 9% and 17%. Decreased yearly bias correction corresponded with years of high rainfall associated with El Niño. Results for the assessments of the effectiveness of the Bayesian approach showed that it was more effective in reducing systematic errors related to rainfall magnitudes, but its performance decreased in areas of sparse rain gauge network that insufficiently represented rainfall variabilities. This affected areas of deep convection, leading to minimal overestimation reductions associated with the cirrus effect. Conversely, significant corrections were during years of low rainfall from shallow convections. The approach is suitable for long-term applications where consistencies of mean errors can be assumed