Minimizing the Impacts of Desertification in an Arid Region: A Case Study of the West Desert of Iraq

Abstract

Currently, desertification is a major problem in the western desert of Iraq. The harsh nature, remoteness, and size of the desert make it difficult and expensive to monitor and mitigate desertification. Therefore, this study proposed a comprehensive and cost-effective method, via the integration of geographic information systems (GISs) and remote sensing (RS) techniques to estimate the potential risk of desertification, to identify the most vulnerable areas and determine the most appropriate sites for rainwater conservation. Two indices, namely, the Normalized Differential Vegetation Index (NDVI) and Land Degradation Index (LDI), were used for a cadastral assessment of land degradation. The findings of the combined rainwater harvesting appropriateness map, and the maps of NDVI and LDI changes found that 65% of highly suitable land for rainwater harvesting lies in the large change and 35% lies in the small change of NDVI, and 85% of highly suitable land lies in areas with a moderate change and 12% lies in strong change of LDI. The adoption of the weighted linear combination (WLC) and Boolean methods within the GIS environment, and the analysis of NDVI with LDI changes can allow hydrologists, decision-makers, and planners to quickly determine and minimize the risk of desertification and to prioritize the determination of suitable sites for rainwater harvesting.

Groundwater Quality and Sustainability Evaluation for Irrigation Purposes: A Case Study in an Arid Region, Iraq

“…It is classified as an arid region [15]. The central part of groundwater is renewable and recharged in limited flood areas in the desert only [16]. In the last two decades, Iraq has been suffering from insufficient water resources to meet population demands because of climate change and the dam's project in Syria and Turkey (Southeastern Anatolia Project -Turkey by constructing 22 dams on Tigris and Euphrates River).…”

Geospatial Technique Integrated with MCDM Models for Selecting Potential Sites for Harvesting Rainwater in the Semi-arid Region

“…Decision-makers begin with a worst-case scenario in which all criteria are set to the lowest potential levels. Then they choose the most crucial criterion by choosing the one that, if improved, would have the most impact on the total situation (Sayl et al, 2021). 4.…”

“…The eigenvectors for each row are produced using geometric principles by multiplying the value for each criterion in each column in the same row of the original pairwise comparison matrix and then applying it to each row, as formulated below (Pecchia et al, 2011;Sayl et al, 2021).…”

“…The priority vector is calculated by dividing the eigenvalues by their total and normalizing them to 1. The maximum lambda (max) is calculated by adding the products of each element of the priority vector and the sum of the reciprocal matrix's columns, as indicated in the formula below (Eryiğit & Sulaiman, 2022;Pecchia et al, 2011;Sayl et al, 2021).…”

“…The maximum lambda (λmax) is calculated by adding the products of each element of the priority vector and the sum of the reciprocal matrix's columns, as illustrated in the following formula (Alkaradaghi et al, 2019;Pecchia et al, 2011;Sayl et al, 2021):…”