Investigating Decadal Changes of Multiple Hydrological Products and Land-Cover Changes in the Mediterranean Region for 2009–2018

Abstract

Land-cover change is a critical concern due to its climatic, ecological, and socioeconomic consequences. In this study, we used multiple variables including precipitation, vegetation index, surface soil moisture, and evapotranspiration obtained from different satellite sources to study their association with land-cover changes in the Mediterranean region. Both observational and modeling data were used for climatology and correlation analysis. Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Global Land Data Assimilation System (GLDAS) were used to extract surface soil moisture and evapotranspiration data. Intercomparing the results of FLDAS and GLDAS suggested that FLDAS data had better accuracy compared to GLDAS for its better coherence with observational data. Climate Hazards Group Infra-Red Precipitation with Station Data (version 2.0 final) (CHIRPS Pentad) were used to extract precipitation data while Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to extract the vegetation indices used in this study. The land-cover change detection was demonstrated during the 2009–2018 period using MODIS Land-Cover data. Some of the barren and crop lands in Euphrates-Tigris and Algeria have converted to low-vegetated shrublands over the time, while shrublands and barren areas in Egypt’s southwestern Delta region became grasslands. These observations were well explained by changing trends of hydrological variables which showed that precipitation and soil moisture had higher values in the countries located to the east of the Mediterranean region compared to the ones on the west. For evapotranspiration, the countries in the north had lower values except for countries in Europe such as Bosnia, Romania, Slovenia, and countries in Africa such as Egypt and Libya. The enhanced vegetation index appeared to be decreasing from north to south, with countries in the north such as Germany, Romania, and Czechia having higher values, while countries in the south such as Libya, Egypt, and Iraq having lower trends. Time series analysis for selected countries was also done to understand the change in hydrological parameters, including Enhanced Vegetation Index, evapotranspiration, and soil moisture, which showed alternating drop and rise as well as stagnant values for different parameters in each country.

Analysis and Prediction of Terrestrial Water Storage Anomalies in the Lower Yangtze River Basin Based on an Improved Grey-Markov Model

“…The applicability of the GLDAS data in China has been proven by many researchers over the past few years (Hu et al ., 2021; Li et al ., 2021; Liu et al ., 2019b; Qi et al ., 2020). The results show that the Noah model data are of good quality in wet areas of China.…”

“…The model drives four land-surface process models (i.e. Mosaic, Noah, 2002-4 to 2021-4 1979-1 to 2021-4 Missing Data 20022002-07;2003-06;2011-01;2011-06;2012-05;2012-10;2013-03;2013-08;2013-09;2014-02;2014-07;2014-12;2015-06;2015-10;2015-11;2016-04;2016-09;2016-10;2017-02;2017-07;2017-08;2017-09;2017-10;2017-11;2017-12;2018-01;2018-02;2018-03;2018-04;2018-05; The applicability of the GLDAS data in China has been proven by many researchers over the past few years (Hu et al, 2021;Li et al, 2021;Liu et al, 2019b;Qi et al, 2020). The results show that the Noah model data are of good quality in wet areas of China.…”

Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine

“…The soil moisture data used in this study comprised the FLDAS datasets, which contain global monthly soil moisture data in different depths from 1982 to 2020, as well as information on elevation, vegetation coverage, and albedo. These datasets were designed to promote the needs of environmental and food security assessments in developing countries where data are scarce; the spatial resolution is 0.1 degrees [48,49]. The FLDAS datasets can be obtained through the GEE platform (https://developers.google.com/earth-engine/ datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001; accessed on 20 October 2021).…”

A Remotely Sensed Study of the Impact of Meteorological Parameters on Vegetation for the Eastern Basins of Afghanistan

“…The purpose of the Global Land Data Assimilation System (GLDAS) was to employ a source of data for the assessment of the environmental and food security in developing countries, such as Afghanistan, that do not have access to terrestrial data [47,48]. The overall goal of the GLDAS model was to drive multiple offline LSMs and integrate large amounts of observation-based data, to be implemented globally with high resolution.…”