A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modelin
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ORIGINAL PAPER
A framework for climate change assessment in Mediterranean data-sparse watersheds using remote sensing and ARIMA modeling Mario J. Al Sayah 1,2,3 & Chadi Abdallah 1
&
Michel Khouri 2 & Rachid Nedjai 3 & Talal Darwich 1
Received: 5 March 2019 / Accepted: 15 October 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract This study aims to propose a framework for assessing climate change in Mediterranean data-sparse contexts. For that purpose, the 309-km2 Lebanese Nahr Ibrahim watershed, extending over 3% of Lebanon’s surface, was chosen as a representative of the targeted settings. Generally, holistic climate change assessments encompass both climate trend analysis and future forecasting. According to the World Meteorological Organization, a continuous, homogenous, and uninterrupted climatic record for at least 30 years is needed to fulfill these tasks. Often, some Mediterranean watersheds lack such data and are hence characterized by climatic data scarcity. Such is the case of Lebanon where 30 years of wars have considerably disrupted the country’s climatic record. In an effort to overcome this state of data scarcity, remote sensing–derived drought indicators were used to determine the climate’s evolution during the last 28 years. For that purpose, several remote sensing indices were extracted from LANDSAT imageries for the period 1990–2018 at a 3-year interval, and were coupled to meteorological indicators. Forecasting was then performed using autoregressive integrated moving average (ARIMA) models. Meteorological indices showed increased variability of precipitations and aridity periods, while remote sensing indicators collectively revealed slight shifts towards increasing droughts. Projections using ARIMA models forecasted increases of 0.9 °C, 0.7 °C, and 0.8 °C for average, maximal, minimal temperatures, and an average 6 mm decrease of precipitations at the 95% confidence level for the year 2030. The presented approach can serve as a tool for proactive climate change mitigation or adaptation plans.
1 Introduction Climate change (CC) has been considered as the most threatening challenge of the twenty-first century (Mamuye and Kebebewu 2018). Since the adoption of the United Nations Framework on Climate Change in 1992 (UNFCC 1992), the global response to face CC amplifications due to increased anthropogenic effects has intensified. Fears from impacts at the environmental, socioeconomic, ecosystem, and human
welfare scales have significantly increased (IPCC 2014a), and the need to address CC became a must under different disciplines. CC manifestations have been interpreted by atmospheric scientists as increasing trends of global warming, and a continuum of temperature increases coupled to changes in precipitation patterns (Li and Fang 2017; Terray and Boé 2013). Depending on various greenhouse gas emission scenarios, climatic models estimate that average global temperatures are to increase by 1.8 to 4.0 °C by the end of the twenty-
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