Multi-Criteria Evaluation of Irrigated Agriculture Suitability to Achieve Food Security in an Arid Environment

Abstract

This research aims at assessing land suitability for large-scale agriculture using multiple spatial datasets which include climate conditions, water potential, soil capabilities, topography and land management. The study case is in the Emirate of Abu Dhabi, in the UAE. The aridity of climate in the region requires accounting for non-renewable sources like desalination and treated sewage effluent (TSE) for an accurate and realistic assessment of irrigated agriculture suitability. All datasets were systematically aggregated using an analytical hierarchical process (AHP) in a GIS model. A hierarchal structure is built and pairwise comparisons matrices are used to calculate weights of the criteria. All spatial processes were integrated to model land suitability and different types of crops are considered in the analysis. Results show that jojoba and sorghum show the best capabilities to survive under the current conditions, followed by date palm, fruits and forage. Vegetables and cereals proved to be the least preferable options. Introducing desalinated water and TSE enhanced land suitability for irrigated agriculture. These findings have positive implications for national planning, the decision-making process of land alteration for agricultural use and addressing sustainable land management and food security issues.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

“…The main goal of the work is to better understand the limitations of the two models in a hyperarid environment, where numerical models are known to underperform and satellite-derived meteorological variables, such as surface temperature, are not very reliable (e.g., Gunwani and Mohan 2017;Ozturk et al 2012;Wehbe et al 2017Wehbe et al , 2018Fonseca et al 2019). This study is particularly relevant as desert regions are predicted to expand in a hypothetical warmer world and are known to be very sensitive to climate change (e.g., Feng et al 2014;Huang et al 2017;Kumar et al 2017;Aldababseh et al 2018). Understanding the reasons behind and quantifying the magnitude of model biases is a necessary step to increase confidence in model projections of climate change.…”

“…Wehbe et al (2019) used both the standalone WRF model and WRF coupled with its hydrological modeling extension package (WRF-Hydro), to investigate the added value of coupled land surface-atmosphere modeling in the simulation of an extreme event in the UAE, which took place in March 2016. This is particularly relevant even during dry periods due to the rapidly changing land surface conditions in the UAE with the expanding green areas and urban development (Aldababseh et al 2018). WRF-Hydro is found to outperform the standalone WRF, even though both models exhibit similar biases.…”

“…Chaouch et al (2017) tested different planetary boundary layer (PBL) parameterization schemes in the simulation of five cold season (January-March) fog events in Abu Dhabi. Local conditions and strong nighttime inversions foster the occurrence of fog events in the UAE (Aldababseh and Temimi 2017). Local PBL schemes were found to outperform nonlocal schemes, with the Quasi-Normal Scale Elimination (QNSE; Sukoriansky et al 2005) giving the best results when the model predicts were evaluated against surface observations and radiosonde profiles.…”

Land Suitability for Sustainable Aquaculture of Rainbow Trout (Oncorhynchus mykiss) in Molinopampa (Peru) Based on RS, GIS, and AHP

“…The square PCMs developed by the specialists (Tables S1-S5) were complemented by columns comprising the stages of standardization, prioritization (standardization of criteria), and matrix consistency check calculations [54]. Prioritization allows us to obtain the Weight of Importance of each sub-criterion and criterion, where the sum of the weights must be equal to 1 per hierarchical group [55].…”

“…In particular, aquaculture production has grown at an average annual rate of 12%, from 28,400 metric tons (MT) in 2006 to just over 100,000 MT in 2017, being rainbow trout (Oncorhynchus mykiss), prawns (Litopenaeus vannamei), fan shell (Argopecten purpuratus) and tilapia (different varieties), species which represent 97% of the total volume harvested [2]. In 2017, trout accounted for 54.63% (54,878.43 MT) of Peruvian aquaculture production, 88.6% of which remained in the national market, consolidating a growth of 339.1% over the last ten years [3]. However, Peruvian aquaculture still has some limitations (technological, commercial, logistics organization, access to credit, technical-professional capacity, network of service providers, and easily accessible goods) that have not allowed its expansion compared to other countries of the region such as Chile, Ecuador, Brazil, and Mexico [2,4].…”