An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions

Drylands cover about 40% of the world’s 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 inte...

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Main Authors: Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy P. Manikandan, Thomas Piechota, Daniele Struppa
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/23/3973
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spelling doaj-3bc89ba531804d3f901b407980fa61512020-12-05T00:04:04ZengMDPI AGRemote Sensing2072-42922020-12-01123973397310.3390/rs12233973An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid RegionsWenzhao Li0Hesham El-Askary1Rejoice Thomas2Surya Prakash Tiwari3Karuppasamy P. Manikandan4Thomas Piechota5Daniele Struppa6Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USASchmid College of Science and Technology, Chapman University, Orange, CA 92866, USAComputational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USACenter for Environment and Water, The Research Institute, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaCenter for Environment and Water, The Research Institute, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaSchmid College of Science and Technology, Chapman University, Orange, CA 92866, USASchmid College of Science and Technology, Chapman University, Orange, CA 92866, USADrylands cover about 40% of the world’s 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’s 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 (2010–2019) 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’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (<inline-formula><math display="inline"><semantics><mi>ψ</mi></semantics></math></inline-formula>), the mean absolute percentage difference (<inline-formula><math display="inline"><semantics><mrow><mrow><mo>|</mo><mi>ψ</mi><mo>|</mo></mrow><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R<sup>2</sup>)).https://www.mdpi.com/2072-4292/12/23/3973drylandsclimate classificationGLDASFLDASmachine learningGoogle Earth Engine
collection DOAJ
language English
format Article
sources DOAJ
author Wenzhao Li
Hesham El-Askary
Rejoice Thomas
Surya Prakash Tiwari
Karuppasamy P. Manikandan
Thomas Piechota
Daniele Struppa
spellingShingle Wenzhao Li
Hesham El-Askary
Rejoice Thomas
Surya Prakash Tiwari
Karuppasamy P. Manikandan
Thomas Piechota
Daniele Struppa
An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
Remote Sensing
drylands
climate classification
GLDAS
FLDAS
machine learning
Google Earth Engine
author_facet Wenzhao Li
Hesham El-Askary
Rejoice Thomas
Surya Prakash Tiwari
Karuppasamy P. Manikandan
Thomas Piechota
Daniele Struppa
author_sort Wenzhao Li
title An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
title_short An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
title_full An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
title_fullStr An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
title_full_unstemmed An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions
title_sort assessment of the hydrological trends using synergistic approaches of remote sensing and model evaluations over global arid and semi-arid regions
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-12-01
description Drylands cover about 40% of the world’s 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’s 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 (2010–2019) 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’ performance in this region, which shows improved results in multiple statistical measures ((RMSE), bias (<inline-formula><math display="inline"><semantics><mi>ψ</mi></semantics></math></inline-formula>), the mean absolute percentage difference (<inline-formula><math display="inline"><semantics><mrow><mrow><mo>|</mo><mi>ψ</mi><mo>|</mo></mrow><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>) and the linear regression coefficients (i.e., slope, intercept, and coefficient of determination (R<sup>2</sup>)).
topic drylands
climate classification
GLDAS
FLDAS
machine learning
Google Earth Engine
url https://www.mdpi.com/2072-4292/12/23/3973
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