Towards an end-to-end analysis and prediction system for weather, climate, and Marine applications in the Red Sea

AbstractThe Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.</jats:p

Survey of Leafhopper Species in Almond Orchards Infected with Almond Witches'-Broom Phytoplasma in Lebanon

Leafhoppers (Hemiptera: Auchenorrhyncha: Cicadellidae) account for more than 80% of all “Auchenorrhynchous” vectors that transmit phytoplasmas. The leafhopper populations in two almond witches'-broom phytoplasma (AlmWB) infected sites: Tanboureet (south of Lebanon) and Bourj El Yahoudieh (north of Lebanon) were surveyed using yellow sticky traps. The survey revealed that the most abundant species was Asymmetrasca decedens, which represented 82.4% of all the leafhoppers sampled. Potential phytoplasma vectors in members of the subfamilies Aphrodinae, Deltocephalinae, and Megophthalminae were present in very low numbers including: Aphrodes makarovi, Cicadulina bipunctella, Euscelidius mundus, Fieberiella macchiae, Allygus theryi, Circulifer haematoceps, Neoaliturus transversalis, and Megophthalmus scabripennis. Allygus theryi (Horváth) (Deltocephalinae) was reported for the first time in Lebanon. Nested PCR analysis and sequencing showed that Asymmetrasca decedens, Empoasca decipiens, Fieberiella macchiae, Euscelidius mundus, Thamnottetix seclusis, Balclutha sp., Lylatina inexpectata, Allygus sp., and Annoplotettix danutae were nine potential carriers of AlmWB phytoplasma. Although the detection of phytoplasmas in an insect does not prove a definite vector relationship, the technique is useful in narrowing the search for potential vectors. The importance of this information for management of AlmWB is discussed

Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.The development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST

Evaluation of commercial soy sauce koji strains of Aspergillus oryzae for γ-aminobutyric acid (GABA) production

In this study, four selected commercial strains of Aspergillus oryzae were collected from soy sauce koji. These A. oryzae strains designated as NSK, NSZ, NSJ and NST shared similar morphological characteristics with the reference strain (A. oryzae FRR 1675) which confirmed them as A. oryzae species. They were further evaluated for their ability to produce γ-aminobutyric acid (GABA) by cultivating the spore suspension in a broth medium containing 0.4 % (w/v) of glutamic acid as a substrate for GABA production. The results showed that these strains were capable of producing GABA; however, the concentrations differed significantly (P < 0.05) among themselves. Based on the A. oryzae strains, highest GABA concentration was obtained from NSK (194 mg/L) followed by NSZ (63 mg/L), NSJ (51.53 mg/L) and NST (31.66 mg/L). Therefore, A. oryzae NSK was characterized and the sequence was found to be similar to A. oryzae and A. flavus with 99 % similarity. The evolutionary distance (K nuc) between sequences of identical fungal species was calculated and a phylogenetic tree prepared from the K nuc data showed that the isolate belonged to the A. oryzae species. This finding may allow the development of GABA-rich ingredients using A. oryzae NSK as a starter culture for soy sauce production

Position Paper on Water, Energy, Food and Ecosystem (WEFE) Nexus and Sustainable development Goals (SDGs)

The EU and the international community is realising that the Water, Energy, Food and Ecosystem components are interlinked and require a joint planning in order to meet the daunting global challenges related to Water, Energy and Food security and maintaining the ecosystem health and in this way, reach the SDGs. If not dealt with, the world will not be able to meet the demand for water, energy and food in a not too far future and, in any case, in a not sustainable way. The strain on the ecosystems resulting from unsustainable single-sector planning will lead to increasing poverty, inequality and instability. The Nexus approach is fully aligned with and supportive of the EU Consensus on Development. Key elements of the Consensus will require collaborative efforts across sectors in ways that can be supported/implemented by a Nexus approach. In this way, transparent and accountable decision-making, involving the civil society is key and common to the European Consensus on Development and the Nexus approach. The Nexus approach will support the implementation of the SDG in particular SDG 2 (Food), SDG 6 (Water) and SDG 7 (Energy), but most SDGs have elements that link to food, water and energy in one or other way, and will benefit from a Nexus approach. The SDGs are designed to be cross-cutting and be implemented together, which is also reflected in a WEFE Nexus approach. A Nexus approach offers a sustainable way of addressing the effects of Climate Change and increase resilience. The WEFE Nexus has in it the main drivers of climate change (water, energy and food security) and the main affected sectors (water and the environment). Decisions around policy, infrastructure, … developed based on the WEFE Nexus assessments will be suitable as elements of climate change mitigation and adaptation. In fact, it is difficult to imagine solutions to the climate change issue that are not built on a form of Nexus approach. The Nexus approach is being implemented around the world, as examples in the literature demonstrate. These examples together with more examples from EU and member state development cooperation will help build experience that can be consolidated and become an important contribution to a Toolkit for WEFE Nexus Implementation. From the expert discussions, it appears that because of the novelty of the approach, a Toolkit will be an important element in getting the Nexus approach widely used. This should build on experiences from practical examples of NEXUS projects or similar inter-sectorial collaboration projects; and, there are already policy, regulation and practical experience to allow institutions and countries to start applying the Nexus concept.JRC.D.2-Water and Marine Resource

Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons

Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural practices and hydro-power generation. However, the dynamicity, stochasticity and inherent complexities present in the temporal evolution of streamflow could hinder the accurate and reliable forecasting of this important hydrological parameter. In this study, the uncertainty and nonstationary characteristics of streamflow data has been treated using a set of coupled data pre-processing methods before being considered as input for an artificial neural network algorithm namely; rolling mechanism (RM) and grey models (GM). The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. The optimization of the input datasets selection was performed using auto-correlation (ACF) and partial auto-correlation (PACF) functions. The pre-processed data was then integrated with two artificial neural network models, the back propagation (RMGM-BP) and Elman Recurrent Neural Network (RMGM-ERNN). The development, training, testing and evaluation of the proposed hybrid models were undertaken using streamflow data for two tropical hydrological basins (Johor and Kelantan Rivers). The hybrid RMGM-ERNN was found to provide better results than the hybrid RMGM-BP model. Relatively good performance of the proposed hybrid models with a data pre-processing approach provides a successful alternative to achieve better accuracy in streamflow forecasting compared to the traditional artificial neural network approach without a data pre-processing scheme

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a novel combination of the ANFIS model with the algorithm as an optimizer tool to construct a hybrid ANFIS-FFA model. The results of the ANFIS-FFA model is compared with the classical ANFIS model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy Inference Systems (FIS) generation. The historicalmonthly streamflow data for Pahang River, which is a major river system in Malaysia that characterized by highly stochastic hydrological patterns, is used in the study. Sixteen different input combinations with one to five time-lagged input variables are incorporated into the ANFIS-FFA and ANFIS models to consider the antecedent seasonal variations in historical streamflow data. The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (r) are used to evaluate the forecasting performance of ANFIS-FFA model. In conjunction with these metrics, the refined Willmott’s Index (Drefined), Nash-Sutcliffe coefficient (ENS) and Legates and McCabes Index (ELM) are also utilized as the normalized goodness-of-fit metrics. Comparison of the results reveals that the FFA is able to improve the forecasting accuracy of the hybrid ANFIS-FFA model (r = 1; RMSE = 0.984; MAE = 0.364; ENS = 1; ELM = 0.988; Drefined = 0.994) applied for the monthly streamflow forecasting in comparison with the traditional ANFIS model (r = 0.998; RMSE = 3.276; MAE = 1.553; ENS = 0.995; ELM = 0.950; Drefined = 0.975). The results also show that the ANFIS-FFA is not only superior to the ANFIS model but also exhibits a parsimonious modelling framework for streamflow forecasting by incorporating a smaller number of input variables required to yield the comparatively better performance. It is construed that the FFA optimizer can thus surpass the accuracy of the traditional ANFIS model in general, and is able to remove the false (inaccurately) forecasted data in the ANFIS model for extremely low flows. The present results have wider implications not only for streamflow forecasting purposes, but also for other hydro-meteorological forecasting variables requiring only the historical data input data, and attaining a greater level of predictive accuracy with the incorporation of the FFA algorithm as an optimization tool in an ANFIS model