Can marine fisheries and aquaculture meet fish demand from a growing human population in a changing climate?

Expansion in the world's human population and economic development will increase future demand for fish products. As global fisheries yield is constrained by ecosystems productivity and management effectiveness, per capita fish consumption can only be maintained or increased if aquaculture makes an increasing contribution to the volume and stability of global fish supplies. Here, we use predictions of changes in global and regional climate (according to IPCC emissions scenario A1B), marine ecosystem and fisheries production estimates from high resolution regional models, human population size estimates from United Nations prospects, fishmeal and oil price estimations, and projections of the technological development in aquaculture feed technology, to investigate the feasibility of sustaining current and increased per capita fish consumption rates in 2050. We conclude that meeting current and larger consumption rates is feasible, despite a growing population and the impacts of climate change on potential fisheries production, but only if fish resources are managed sustainably and the animal feeds industry reduces its reliance on wild fish. Ineffective fisheries management and rising fishmeal prices driven by greater demand could, however, compromise future aquaculture production and the availability of fish products.     

Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations

Evapotranspiration estimation is of crucial importance in arid and hyper-arid regions, which suffer from water shortage, increasing dryness and heat. A modeling study is reported here to cross-station assessment between hyper-arid and humid conditions. The derived equations estimate ET₀ values based on temperature-, radiation-, and mass transfer-based configurations. Using data from two meteorological stations in a hyper-arid region of Iran and two meteorological stations in a humid region of Spain, different local and cross-station approaches are applied for developing and validating the derived equations. The comparison of the gene expression programming (GEP)-based-derived equations with corresponding empirical-semi empirical ET₀ estimation equations reveals the superiority of new formulas in comparison with the corresponding empirical equations. Therefore, the derived models can be successfully applied in these hyper-arid and humid regions as well as similar climatic contexts especially in data-lack situations. The results also show that when relying on proper input configurations, cross-station might be a promising alternative for locally trained models for the stations with data scarcity.

     

Rock cut stability assessment in mountainous regions

Ensuring stability of rock slopes is an essential requirement in the progress of our societies today. Rock determined to be loose or with potential for failure must be removed or restrained in some way. In our work, after doing an inventory of the instabilities that occurred in the last 5 years in the Basque Country, we analyse the different factors, in slope stability. The potential for failure is evaluated for different classes of rock mass, characterized previously by their geomechanical properties. The characterization of potential risk of each one is undertaken by considering 10 parameters that define the nature of mass rock, relative orientation and morphological features of the slope (interaction rock massif-slope) and infrastructure features (interaction rock massif-slope-infrastructure). Each of these parameters is evaluated separately and a Risk Factor (RF) is determined. The RF reaches a maximum value of 10,000 and allows to differentiate four categories of slopes; each category has its own priority. Rock mass characteristics also determine the potential damage from instability and the associated correction measures. The systematic evaluation of instabilities must allow establishing a priority in the correction measures and thus optimise the available economic resources.     

Geomechanical characterisation of rock masses in Alpine regions: the Basque Arc (Basque-Cantabrian basin, Northern Spain)

The geomechanical characterisation of rock masses in Alpine regions is one of the main challenges currently facing rock mechanics. These rock masses, made up of very different materials and frequently tectonically disturbed, are affected by engineering works, giving rise to potentially important problems. Thus, in the Basque Country, these materials are at the cause of important fatalities occurring in recent years. In this context, we aim at characterising these materials with the ultimate purpose of predicting their behaviour. The characterisation is initially approached based on the GSI, m_i and σ_ci parameters, following the Hoek-Brown failure criterion. This information is supplemented with field and laboratory measurements, and by obtaining the RQD and RMR indices. Using the information obtained from 99 metering stations, we develop a graphic classification procedure, which allows us to distinguish materials based on their characteristics, behaviour, type and degree of associated problems, and type and effectiveness of solutions provided. Eight classes of material are established.     

New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Accurate estimation of reference evapotranspiration (ET ₀ ) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET ₀ estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET ₀ ) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET ₀ estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET ₀ estimation in West Africa.     

Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

The work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflectance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.     

Predicting daily pan evaporation by soft computing models with limited climatic data

Abstract

Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.

Editor D. Koutsoyiannis