On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Determining representative sample size for validation of continuous, large continental remote sensing data

The validation of global remote sensing data comprises multiple methods including comparison to field measurements, cross-comparisons and verification of physical consistency. Physical consistency and cross-comparisons are typically assessed for all pixels of the entire product extent, which requires intensive computing. This paper proposes a statistically representative sampling approach to reduce time and efforts associated with validations of remote sensing data having big data volume. A progressive sampling approach, as typically applied in machine learning to train algorithms, combined with two performance measures, was applied to estimate the required sample size. The confidence interval (CI) and maximum entropy probability distribution were used as indicators to represent accuracy. The approach was tested on 8 continental remote sensing-based data products over the Middle East and Africa. Without the consideration of climate classes, a sample size of 10,000–100,000, dependent on the product, met the nominally set CI and entropy indicators. This corresponds to <0.01 % of the total image for the high-resolution images. All continuous datasets showed the same trend of CI and entropy with increasing sample size. The actual evapotranspiration and interception (ETIa) product was further analysed based on climate classes, which increased the sample size required to meet performance requirements, but was still determined to be significantly less than the entire dataset size. The proposed approach can significantly reduce the processing time while still providing a statistically valid representation of a large remote sensing dataset. This can be useful as more high-resolution remote sensing data becomes available

Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review

The scarcity of water and the growing global food demand has fevered the debate on how to increase agricultural production without further depleting water resources. Crop water productivity (CWP) is a performance indicator to monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and its components, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ET a), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users and developers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First, the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy, is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracy benchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7% to 67%, depending on method and scale. The error of CWP from remote sensing ranges from 7% to 22%, based on the highest reported performing remote sensing products. However, when considering the entire breadth of reported crop yield and ET a accuracy, the achievable errors propagate to CWP ranges of 74% to 108%. Although the remote sensing CWP appears comparable to the accuracy of in-situ methods in many cases, users should determine whether it is suitable for their specific application of CWP

Water scarcity alleviation through water footprint reduction in agriculture: The effect of soil mulching and drip irrigation

Water scarcity has received global attention in the last decade as it challenges food security in arid and semi-arid regions, particularly in the Middle East and North Africa. This research assesses the possible alleviation of water scarcity by reducing the water footprint in crop production through the application of soil mulching and drip irrigation. The study is the first to do so at catchment scale, taking into account various crops, multi-cropping, cropping patterns, and spatial differences in climate, soil, and field management factors, using field survey and local data. The AquaCrop-OS model and the global water footprint assessment (WFA) standard were used to assess the green and blue water footprint (WF) of ten major crops in the Upper Litani Basin (ULB) in Lebanon. The blue water saving and blue water scarcity reduction under these two alternative practices were compared to the current situation. The results show that the WF of crop production is more sensitive to climate than soil type. The annual blue WF of summer crops was largest when water availability was lowest. Mulching reduced the blue WF by 3.6% and mulching combined with drip irrigation reduced it by 4.7%. The blue water saving from mulching was estimated about 6.3 million m3/y and from mulching combined with drip irrigation about 8.3 million m3/y. This is substantial but by far not sufficient to reduce the overall blue WF in summer to a sustainable level at catchment scale

Evaluation of WaPOR V2 evapotranspiration products across Africa

The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management

Reduce blue water scarcity and increase nutritional and economic water productivity through changing the cropping pattern in a catchment

Water-stressed countries need to plan their food security and reduce the pressure on their limited water resources. Agriculture, the largest water-using sector, has a major role in addressing water scarcity and food security challenges. While there has been quite some attention to water management solutions like soil mulching and improved irrigation, less attention has been paid to adapting the cropping pattern to save water. Here, we investigate how a change in which crops are grown where and when can influence the green and blue water footprint (WF) of crop production, save blue water, reduce blue water scarcity and increase both food and cash crop production, using FAO's AquaCrop model. The performance of two potential solutions, first a strategy of mulching plus drip irrigation, and second a strategy with changing the cropping pattern in addition to mulching and drip irrigation, were compared in one of the most water-stressed catchments in the world, the Upper Litani Basin in Lebanon. Our results show a substantial potential for more efficient use of green water resources for food production while saving scarce blue water resources. Whereas mulching and drip irrigation together decrease the blue WF in the basin by 4.5%, changing the cropping pattern as well can decrease it by 20.3%. Food and cash production could increase by 3% and 50% by changing the cropping pattern, compared to 1.5% and 2.1% by mulching and drip irrigation. Changing the cropping pattern could thus significantly reduce water scarcity and enlarge food and cash production in the basin. © 2020Authors are thankful for the support from the UN-FAO headquarter and FAO-Lebanon. The ground data used for the simulation and modelling were collected during the field visit of the Litani Basin funded by the FAO-WaPOR project (FRAME consortium

An integrative information aqueduct to close the gaps between satellite observation of water cycle and local sustainable management of water resources

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results