{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T23:13:56Z","timestamp":1773443636539,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T00:00:00Z","timestamp":1607040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drylands cover about 40% of the world\u2019s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people\u2019s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010\u20132019) using an integrative approach of remotely sensed and physical process-based numerical modeling (e.g., Global Land Data Assimilation System (GLDAS) and Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) models) data. Interaction between hydrological and ecological indicators including precipitation, evapotranspiration, surface soil moisture and vegetation indices are presented in the global four types of arid and semi-arid areas. The trends followed by precipitation, evapotranspiration and surface soil moisture over the decade are also mapped using harmonic analysis. This study also shows that some hotspots in these global drylands, which exhibit different processes of land cover change, demonstrate strong coherency with noted groundwater variations. Various types of statistical measures are computed using the satellite and model derived values over global arid and semi-arid regions. Comparisons between satellite- (NASA-USDA Surface Soil Moisture and MODIS Evapotranspiration data) and model (FLDAS and GLDAS)-derived values over arid regions (BSh, BSk, BWh and BWk) have shown the over and underestimation with low accuracy. Moreover, general consistency is apparent in most of the regions between GLDAS and FLDAS model, while a strong discrepancy is also observed in some regions, especially appearing in the Nile Basin downstream hyper-arid region. Data-driven modelling approaches are thus used to enhance the models\u2019 performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (\u03c8), the mean absolute percentage difference (|\u03c8|)) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R2)).<\/jats:p>","DOI":"10.3390\/rs12233973","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T11:59:00Z","timestamp":1607083140000},"page":"3973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4366-4985","authenticated-orcid":false,"given":"Wenzhao","family":"Li","sequence":"first","affiliation":[{"name":"Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-3705","authenticated-orcid":false,"given":"Hesham","family":"El-Askary","sequence":"additional","affiliation":[{"name":"Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"},{"name":"Center of Excellence in Earth Systems Modeling and Observations, Chapman University, CA 92866, USA"},{"name":"Department of Environmental Sciences, Faculty of Science, Alexandria University, Moharem Bek, Alexandria 21522, Egypt"}]},{"given":"Rejoice","family":"Thomas","sequence":"additional","affiliation":[{"name":"Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8574-3959","authenticated-orcid":false,"given":"Surya Prakash","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Center for Environment and Water, The Research Institute, King Fahd University of Petroleum &amp; Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"given":"Karuppasamy P.","family":"Manikandan","sequence":"additional","affiliation":[{"name":"Center for Environment and Water, The Research Institute, King Fahd University of Petroleum &amp; Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4548-7847","authenticated-orcid":false,"given":"Thomas","family":"Piechota","sequence":"additional","affiliation":[{"name":"Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}]},{"given":"Daniele","family":"Struppa","sequence":"additional","affiliation":[{"name":"Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"ref_1","unstructured":"Assessment, M.E. 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