Global Drought and Flood E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Geophysical Monograph Series
- Sprache: Englisch
Recent advances in the modeling and remote sensing of droughts and floods
Droughts and floods are causing increasing damage worldwide, often with devastating short- and long-term impacts on human society. Forecasting when they will occur, monitoring them as they develop, and learning from the past to improve disaster management is vital.
Global Drought and Flood: Observation, Modeling, and Prediction presents recent advances in the modeling and remote sensing of droughts and floods. It also describes the techniques and products currently available and how they are being used in practice.
Volume highlights include:
- Remote sensing approaches for mapping droughts and floods
- Physical and statistical models for monitoring and forecasting hydrologic hazards
- Features of various drought and flood systems and products
- Use by governments, humanitarian, and development stakeholders in recent disaster cases
- Improving the collaboration between hazard information provision and end users
The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Find out more about this book from this Q&A with the Author.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
List Of Contributors
Preface
Part I: Remote Sensing for Global Drought and Flood Observations
1 Progress, Challenges, and Opportunities in Remote Sensing of Drought
1.1. INTRODUCTION
1.2. PROGRESS IN REMOTE SENSING OF DRIVERS OF DROUGHT
1.3. MULTI‐INDICATOR DROUGHT MODELING
1.4. DROUGHT AND HEATWAVES FEEDBACKS
1.5. REMAINING CHALLENGES AND OPPORTUNITIES
1.6. CONCLUSION
REFERENCES
2 Remote Sensing of Evapotranspiration for Global Drought Monitoring
2.1. INTRODUCTION
2.2. HISTORICAL SKETCH OF ET REMOTE SENSING STUDIES AND ET DATA PRODUCTS
2.3. ESTIMATING ET AND MONITORING DROUGHT WITH GEOSTATIONARY SATELLITE THERMAL OBSERVATIONS
2.4. DROUGHT MONITORING PRODUCT SYSTEM BASED ON ET REMOTE SENSING
2.5. COMBINING ET REMOTE SENSING WITH MICROWAVE SOIL MOISTURE DATA FOR DROUGHT MONITORING
2.6. DISCUSSION
ACKNOWLEDGMENTS
REFERENCES
3 Drought Monitoring Using Reservoir Data Collected via Satellite Remote Sensing
3.1. INTRODUCTION
3.2. DROUGHT MONITORING USING REMOTELY SENSED RESERVOIR DATA
3.3. ADOPTING REMOTELY SENSED RESERVOIR DATA TO SUPPORT DROUGHT MODELING APPLICATIONS
3.4. FUTURE DIRECTIONS
3.5. DISCUSSION AND CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
4 Automatic Near‐Real‐Time Flood Mapping from Geostationary Low Earth Orbiting Satellite Observations
4.1. INTRODUCTION
4.2. DATA USED
4.3. METHODS
4.4. APPLICATIONS
4.5. VALIDATION
4.6. DISCUSSION
4.7. SUMMARY
ACKNOWLEDGMENTS
REFERENCES
5 Global Flood Observation with Multiple Satellites: Applications in Rio Salado (Argentina) and the Eastern Nile Basin
5.1. INTRODUCTION: THE STATE OF THE SCIENCE AND NEED FOR GLOBAL SATELLITE FLOOD MAPPING
5.2. METHODS FOR GLOBAL FLOOD OBSERVATION
5.3. WATERSHED CASE STUDIES: ARGENTINA AND THE EASTERN NILE REGION
5.4. RESULTS FROM FLOOD MAPPING IN CASE STUDIES
5.5. LIMITATIONS AND FUTURE DIRECTIONS FOR THE UTILITY OF SATELLITE FLOOD‐EVENT DATA
5.6. CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
6 Integrating Earth Observation Data of Floods with Large‐Scale Hydrodynamic Models
6.1. INTRODUCTION
6.2. EARTH OBSERVATION FLOOD DATA
6.3. INTEGRATION OF EO DATA AND FLOOD MODELS
6.4. OUTLOOK
6.5. CONCLUSION
REFERENCES
Part II: Modeling and Prediction of Global Drought and Flood
7 Global Integrated Drought Monitoring with a Multivariate Framework
7.1. INTRODUCTION
7.2. METHOD
7.3. DATA
7.4. RESULTS
7.5. CONCLUSION
REFERENCES
8 A Probabilistic Framework for Agricultural Drought Forecasting Using the Ensemble Data Assimilation and Bayesian Multivariate Modeling
8.1. INTRODUCTION
8.2. REVIEW OF CURRENT DROUGHT FORECASTING SYSTEMS
8.3. THE PROPOSED COUPLED DYNAMICAL–STATISTICAL DROUGHT FORECASTING SYSTEM
8.4. CASE STUDIES
8.5. CONCLUSIONS AND DISCUSSION
REFERENCES
9 Integrating Soil Moisture Active/Passive Observations with Rainfall Data Using an Analytic Model for Drought Monitoring at the Continental Scale
9.1. INTRODUCTION
9.2. DATA AND METHOD
9.3. RESULTS
9.4. DISCUSSION AND CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
10 Global Flood Models
10.1. INTRODUCTION
10.2. TYPES OF GFM AND SPECIFIC EXAMPLES
10.3. APPLICATIONS OF GLOBAL FLOOD MODELS
10.4. INSURANCE CATASTROPHE MODELS
10.5. GFM CREDIBILITY
10.6. THE FUTURE OF GFMS
REFERENCES
11 Calibration of Global Flood Models: Progress, Challenges, and Opportunities
11.1. INTRODUCTION
11.2. GLOBAL HYDROLOGICAL MODEL CALIBRATION
11.3. MAIN CHALLENGES OF CALIBRATING GLOBAL HYDROLOGICAL MODELS
11.4. EMERGING OPPORTUNITIES
11.5. SUMMARY
REFERENCES
12 Digital Elevation Model and Drainage Network Data Sets for Global Flood and Drought Modeling
12.1. INTRODUCTION
12.2. GLOBAL BASELINE DIGITAL ELEVATION DATA FOR HYDROLOGICAL MODELING
12.3. GLOBAL HYDROGRAPHY DATA SETS
12.4. CHALLENGES AND OPPORTUNITIES
12.5. SUMMARY
ACKNOWLEDGMENTS
REFERENCES
13 Fundamental Data Set for Global Drought and Flood Modeling
13.1. INTRODUCTION
13.2. GLOBAL LAND COVER DATA SETS
13.3. DISCUSSION
REFERENCES
Part III: Global Drought and Flood Risk Assessment, Management, and Socioeconomic Response
14 Global River Flood Risk Under Climate Change
14.1. INTRODUCTION
14.2. MODELING GLOBAL RIVER FLOOD RISK: GENERAL CONCEPTS AND METHODS
14.3. THE GLOFRIS MODELING FRAMEWORK
14.4. CAMA‐FLOOD AND ISIMIP MODELING FRAMEWORKS
14.5. THE GAR‐2015 FLOOD RISK FRAMEWORK
14.6. THE JOINT RESEARCH CENTRE MODEL
14.7. OTHER FLOOD RISK MODELS
14.8. CONCLUSIONS
REFERENCES
15 Direct Tangible Damage Classification and Exposure Analysis Using Satellite Images and Media Data
15.1. INTRODUCTION
15.2. DATA AND STUDY SITE
15.3. METHOD
15.4. RESULTS
15.5. DISCUSSION
15.6. CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
16 Flood Risk and Monitoring Data for Preparedness and Response
16.1. INTRODUCTION
16.2. CHALLENGES IN UNDERSTANDING AND TRUSTING FLOOD DATA
16.3. TWO CASE STUDIES FRAMING THE DISCONNECT BETWEEN FLOOD DATA DEVELOPERS AND DECISION MAKERS
16.4. IDENTIFICATION OF COMMON THEMES FOUND IN THE QUESTIONS ASKED WITHIN THE CASE STUDIES
16.5. SUGGESTED OPPORTUNITIES TO MOVE TOWARDS NARROWING THE GAP
16.6. CONCLUSION
ACKNOWLEDGMENTS
REFERENCES
17 Global Flood Partnership
*
17.1. INTRODUCTION
17.2. MODELS AND PRODUCTS
17.3. GFP ACTIVATIONS
17.4. DISCUSSION AND CONCLUSIONS
REFERENCES
18 Drought and Flood Monitoring and Forecasting
18.1. REMOTE SENSING FOR DROUGHT AND FLOOD MODELING
18.2. DROUGHT AND FLOOD MODELING
18.3. RISK ANALYSIS AND COLLABORATION
18.4. PERSPECTIVE
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Major inputs data for GET‐D system
Table 2.2 GET‐D system outputs
Chapter 4
Table 4.1 Comparison Between MODIS Flood Product and VIIRS Flood Product
Chapter 5
Table 5.1 Summary of Antecedent Remote Sensing Products for Surface Water Cha...
Table 5.2 Summary of Publicly Available Satellite Imagery Used for the Observ...
Table 5.3 Summarized Accuracy Statistics for 13 Flood Events Comparing High‐R...
Chapter 9
Table 9.1 Root Mean Square Error (RMSE) and Correlation Coefficient (R) of Mo...
Chapter 10
Table 10.1 Characteristics of Global Flood Models Relative to Traditional Loc...
Table 10.2 Global Flood Model Details
Table 10.3 Different Possible Applications of Global Flood Models with Refere...
Chapter 12
Table 12.1 Selected DEM Data Available Globally or Near Globally
Table 12.2 Selected Available Global Hydrography Database
Chapter 13
Table 13.1 Summary of the Land‐ Cover Data Sets.
Chapter 14
Table 14.1 List of risk models considered and their main characteristics
Chapter 15
Table 15.1 Specifications of the Satellite GF‐1 and Satellite GF‐2 Images
Table 15.2 Data Acquisition and Image Quality Assessment
Table 15.3 Classification of Building Damage
Table 15.4 Floor Areas of Different Damaged Buildings
Table 15.5 Classification of Road Damage
Table 15.6 Classification of Farmland Damage
Chapter 17
Table 17.1 A Nonexhaustive List of GFP Flood Products Regularly Updated on th...
List of Illustrations
Chapter 1
Figure 1.1 Rainfall map by NASA’s Tropical Rainfall Measuring Mission (TRMM)...
Figure 1.2 Near real‐time 0.04° precipitation information provided by the Gl...
Figure 1.3 Near real‐time drought monitoring and prediction system by the Gl...
Figure 1.4 Soil moisture observation by NASA’s Soil Moisture Active Passive ...
Figure 1.5 Standardized Relative Humidity Index (SRHI) for (a) August 2010, ...
Figure 1.6 Three‐month Standardized Precipitation Evapotranspiration Index (...
Figure 1.7 Evaporative Stress Index (ESI) derived from observations of land ...
Figure 1.8 A below‐normal snowpack observed by the Moderate Resolution Imagi...
Figure 1.9 Global map of annual water storage change for the period of 2002–...
Figure 1.10 Near real‐time drought monitoring and prediction system by the G...
Figure 1.11 Temperature anomalies by the Moderate Resolution Imaging Spectro...
Chapter 2
Figure 2.1 (a) A schematic description of the surface‐layer component of the...
Figure 2.2 The GOES evapotranspiration and drought product (GET‐D) system de...
Figure 2.3 The 2/4/8/12‐week composite of ESI generated from the GET‐D syste...
Figure 2.4 The unique characteristics of Rapid Change Index (RCI) values for...
Figure 2.5 The procedure for constructing the BDI_b using the RMSEs estimate...
Figure 2.6 Monthly BDI_b for the Russian (from 40°N, 20°E to 70°N, 80°E) dom...
Figure 2.7 Monthly BDI_b for the southern United States (from 25°N, −115°W t...
Chapter 3
Figure 3.1 Time series of Lake Powell elevation variations based on observat...
Figure 3.2 (a) Monthly average precipitation and SPI with a 6‐month timescal...
Figure 3.3 Storage variations of Lake Powell estimated using radar altimetry...
Figure 3.4 Comparison of remotely sensed surface area with observed storage/...
Figure 3.5 (a) Monthly average precipitation and SPI with a 6‐month timescal...
Figure 3.6 ICESat‐2 ground tracks for: (a) some natural lakes on the Tibetan...
Chapter 4
Figure 4.1 Plot of the reflectance of different land types in the Vis to SWI...
Figure 4.2 SNPP/VIIRS false‐color images on 17 August (left) and 31 August (...
Figure 4.3 Scatter plots of suprasnow/supra‐ice water (black), suprasnow/sup...
Figure 4.4 An example of a tree structure derived from the C4.5 algorithm....
Figure 4.5 Scatter plots between different combinations of variables for veg...
Figure 4.6 Sketch of a geometric model over an ideal plane.
Figure 4.7 Sketch of a geometric model over a spherical plane.
Figure 4.8 Interdetermination of cloud and cloud shadow using multiple point...
Figure 4.9 (a) A VIIRS false‐color image, (b) a VIIRS flood map without clou...
Figure 4.10 (a) The RMS height (γ) of terrain shadows and (b) the RMS height...
Figure 4.11 (a) The D
mean
of terrain shadows and (b) the D
mean
of floodwater...
Figure 4.12 (a) The D
n
of terrain shadows and (b) the D
n
of floodwaters.
Figure 4.13 (a) A VIIRS false‐color image, (b) a VIIRS flood map without ter...
Figure 4.14 Algorithm flow chart of the VNG Flood V1.0.
Figure 4.15 The GOES‐16/ABI and the SNPP/VIIRS flood detection maps of the W...
Figure 4.16 The GOES‐16/ABI and the SNPP/VIIRS flood detection maps of the W...
Figure 4.17 Joint VIIRS/ABI Flood maps from the GOES‐16/ABI and Suomi‐NPP/VI...
Figure 4.18 Suomi‐NPP/VIIRS flood map of Bangladesh in southern Asia for 23 ...
Figure 4.19 A flood map generated by the United Nations Institute for Traini...
Figure 4.20 SNPP/VIIRS ice‐jam flood detection maps around Galena, Alaska: (...
Figure 4.21 (a) A SNPP/VIIRS false‐color composite image and (b) a SNPP/VIIR...
Figure 4.22 January 2017 floods of California. (a) A MODIS false‐color compo...
Figure 4.23 (a) A MODIS 2‐day and 3‐day composite flood map of California on...
Figure 4.24 Flood maps from Sentinel‐1 and the VIIRS of the West Gulf region...
Figure 4.25 Flood maps and images from Radarsat, Sentinel‐2B, and Suomi‐NPP/...
Figure 4.26 (a) A Landsat‐8 OLI false‐color composite image of Texas, on 6 J...
Figure 4.27 Three pairs of flood maps for comparison between the SNPP/VIIRS ...
Figure 4.28 Scatter plot of supravegetation/suprabare‐soil water detection p...
Figure 4.29 Scatter plot of suprasnow/supra‐ice water detection percentage ...
Chapter 5
Figure 5.1 The general workflow of (a) MODIS imagery processing, (b) water c...
Figure 5.2 Accuracy assessment for 13 flood events comparing precision and r...
Figure 5.3 The general workflow of our Landsat algorithm where we (a) prepro...
Figure 5.4 MODIS (left, red)) versus Landsat (right, light blue) flood exten...
Figure 5.5 Comparing the Feyisa et al. (2014) algorithm (light blue) to the ...
Figure 5.6 Estimated flood extent under various recurrence estimates based o...
Figure 5.7 Nile Basin Dashboard: http://eastern‐nile‐flood‐database.appspot....
Figure 5.8 Flood extent for the October to December 2001 flood occurring in ...
Figure 5.9 The MODIS and Landsat time series of annual inundated area and po...
Figure 5.10 Evidence of canals and dams in the eastern Nile Basin indicating...
Figure 5.11 Percent of watershed area cloud free in the Rio Salado Basin fol...
Figure 5.12 Exceptional flood events in the DFO database: colored areas repr...
Figure 5.13 Number per country of total flood events represented in the DFO ...
Chapter 6
Figure 6.1 Typical flood event maps regularly prepared by the Dartmouth Floo...
Figure 6.2 Images from the Copernicus Sentinel‐1 radar satellite helped to m...
Figure 6.3 Illustration of model calibration and validation results: (a) cal...
Figure 6.4 (top panel) Number of times water was detected between 1987 and 2...
Figure 6.5 River Severn case study. Comparison between forecast and observed...
Chapter 7
Figure 7.1 Time series of SPI, SSI, SRI, and MSDI values during the period 2...
Figure 7.2 Integrated drought monitoring based on the SPI, SSI, SRI, and MSD...
Figure 7.3 Similar to Figure 7.2 but based on drought categories (–1 in the ...
Figure 7.4 Integrated drought monitoring based on (a) the RMSDI and (b) asso...
Chapter 8
Figure 8.1 The framework of the proposed coupled dynamical–statistical droug...
Figure 8.2 The monthly to seasonal drought forecasting framework using the B...
Figure 8.3 Five conditional probability density distributions (PDFs) of drou...
Figure 8.4 The flow diagram of the parallel particle filtering framework (PP...
Figure 8.5 Monthly (top row) to seasonal (bottom row) deterministic drought ...
Figure 8.6 Seasonal deterministic drought forecasts from the current dynamic...
Figure 8.7 Coupled dynamical–statistical probabilistic drought forecasts for...
Figure 8.8 Comparison of the coupled dynamical–statistical probabilistic dro...
Figure 8.9 Comparison of the coupled dynamical–statistical seasonal probabil...
Chapter 9
Figure 9.1 Illustrations of data coverage of SMAP L3_SM_P sensors during an ...
Figure 9.2 A schematic illustration of time series of precipitation and soil...
Figure 9.3 Locations of the six sample grids and distribution of predominant...
Figure 9.4 Modeled soil moisture estimates and retrieved SMAP observations o...
Figure 9.5 The same as Figure 9.4, but for the verification period (8 Februa...
Figure 9.6 Scatter plot between the modeled soil moisture estimates (y axis)...
Figure 9.7 Correlation between modeled soil moisture and the SMAP observatio...
Figure 9.8 Spatial distribution of the model parameters c
1
, c
2
, c
3
, and c
4
. ...
Figure 9.9 Drought conditions expressed as soil moisture percentiles from (l...
Chapter 10
Figure 10.1 Timeline of global flood model (GFM) development highlighting ke...
Figure 10.2 A simplified schematic of the two main model structures used by ...
Figure 10.3 Map depicting the regions where each global flood model (GFM) va...
Figure 10.4 Global flood model agreement across Africa. (a) Aggregated flood...
Chapter 11
Figure 11.1 Schematic showing nested basins where each subbasin was calibrat...
Figure 11.2 The evolution of the objective function (KGE) from generation fi...
Figure 11.3 Streamflow time series for a selected station (River Xingu at Bo...
Figure 11.4 The KGE skill score during calibration and validation periods....
Figure 11.5 The variation of KGE skill score with the bias in the baseline s...
Chapter 12
Figure 12.1 (a) Schematic diagram of the procedures of error removal for gen...
Figure 12.2 The new error‐removed DEM (top), the original DEM (2nd row), the...
Figure 12.3 (a) Example schematic of the DRT algorithm for a hypothetical la...
Figure 12.4 The DRIVE model couples the (left) VIC model and (right) the DRT...
Figure 12.5 Outline of the GFMS providing flood‐intensity estimates and for...
Chapter 13
Figure 13.1 The Global Land Cover Characterization map with International Ge...
Figure 13.2 The University of Maryland global land‐cover map.
Figure 13.3 The Global Land Cover 2000 map.
Figure 13.4 The Moderate‐Resolution Imaging Spectroradiometer land cover for...
Figure 13.5 The GlobCover global land‐cover map.
Figure 13.6 The Global Land Cover by National Mapping Organizations map.
Figure 13.7 The Climate Change Initiative Land Cover global map for the year...
Figure 13.8 The GlobeLand30 land cover map for the 2010 epoch.
Figure 13.9 The SYNLCover global land cover data set.
Figure 13.10 The temporal ranges and spatial resolution of the land‐cover or...
Chapter 14
Figure 14.1 Schematic steps for the flood‐map computation: (left) quantiles ...
Figure 14.2 Schematic view of the latest version of the JRC flood impact mod...
Figure 14.3 Annual estimates of flood damage in the EU in 1990–2013 (in grey...
Chapter 15
Figure 15.1 The study site in Jiangxi Province, China.
Figure 15.2 Xiangyang levee breach in GF‐2 images.
Figure 15.3 The number of CCTV television reports analysed by channels and d...
Figure 15.4 Direct tangible‐damage classification and exposure analysis work...
Figure 15.5 A GF‐1 derived land‐cover map for the study area.
Figure 15.6 Histogram of water index image (a) before and (b) after the cubi...
Figure 15.7 Valleys and peaks in the histogram.
Figure 15.8 (a) The remote sensing image and (b) its water extraction result...
Figure 15.9 Temporal distributions of the surface water extent.
Figure 15.10 Temporal distributions of the flood extent 22 June 2016 to 24 J...
Figure 15.11 (a) The television media image and (b) postflood GF‐2 image.
Figure 15.12 The buildings in the television video.
Figure 15.13 Building damage exposure map.
Figure 15.14 Building damage distribution map.
Figure 15.15 Road damage exposure map.
Figure 15.16 Farmland damage exposure map.
Chapter 16
Figure 16.1 Kutupalong Megacamp for the displaced Rohingya population in sou...
Figure 16.2 A timeline showing climate information linked to specific decisi...
Figure 16.3 A community meeting in Shamlapur, Bangladesh with the Rohingya p...
Figure 16.4 Example of a decision‐making flowchart (DMF) template.
Chapter 17
Figure 17.1 Timeline of a river flood and GFP product types to support disas...
Figure 17.2 The EFI of 48 h accumulated precipitation for 26–27 August 2017 ...
Figure 17.3 July 2017 floods in South China. 1 in 100 year JRC flood map for...
Figure 17.4 Ensemble streamflow predictions from GloFAS forecasts of 4 Augus...
Figure 17.5 The GFMS flood detection estimates over the Ganges–Brahmaputra b...
Figure 17.6 Satellite‐based river discharge estimates using passive microwav...
Figure 17.7 Inundation extent (in red) in Bangladesh during the August 2017...
Figure 17.8 Maximum detected flood extent in Texas and Louisiana, USA, follo...
Guide
Cover Page
Series Page
Title Page
Copyright Page
List Of Contributors
Preface
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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231 Bioenergy and Land Use Change
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233 Global Flood Hazard: Applications in Modeling, Mapping and Forecasting
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235 Electric Currents in Geospace and Beyond
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238 Geological Carbon Storage: Subsurface Seals and Caprock Integrity
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239 Lithospheric Discontinuities
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240 Chemostratigraphy Across Major Chronological Eras
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245 Shale: Subsurface Science and Engineering
Thomas Dewers, Jason Heath, and Marcelo Sánchez (Eds.)
246 Submarine Landslides: Subaqueous Mass Transport Deposits From Outcrops to Seismic Profiles
Kei Ogata, Andrea Festa, and Gian Andrea Pini (Eds.)
247 Iceland: Tectonics, Volcanics, and Glacial Features
Tamie J. Jovanelly
248 Dayside Magnetosphere Interactions
Qiugang Zong, Philippe Escoubet, David Sibeck, Guan Le, and Hui Zhang (Eds.)
249 Carbon in Earth’s Interior
Craig E. Manning, Jung‐Fu Lin, and Wendy L. Mao (Eds.)
250 Nitrogen Overload: Environmental Degradation, Ramifications, and Economic Costs
Brian G. Katz
251 Biogeochemical Cycles: Ecological Drivers and Environmental Impact
Katerina Dontsova, Zsuzsanna Balogh‐Brunstad, and Gaël Le Roux (Eds.)
252 Seismoelectric Exploration: Theory, Experiments, and Applications
Niels Grobbe, André Revil, Zhenya Zhu, and Evert Slob (Eds.)
253 El Niño Southern Oscillation in a Changing Climate
Michael J. McPhaden, Agus Santoso, and Wenju Cai (Eds.)
254 Dynamic Magma Evolution
Francesco Vetere (Ed.)
255 Large Igneous Provinces: A Driver of Global Environmental and Biotic Changes
Richard. E. Ernst, Alexander J. Dickson, and Andrey Bekker (Eds.)
256 Coastal Ecosystems in Transition: A Comparative Analysis of the Northern Adriatic and Chesapeake Bay
Thomas C. Malone, Alenka Malej, and Jadran Faganeli (Eds.)
257 Hydrogeology, Chemical Weathering, and Soil Formation
Allen Hunt, Markus Egli, and Boris Faybishenko (Eds.)
258 At the Doorstep of Our Star: Solar Physics and Solar Wind
Nour E. Raouafi and Angelos Vourlidas (Eds.)
259 Magnetospheres in the Solar System Romain Maggiolo
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Nicolas André, Hiroshi Hasegawa, and Daniel T. Welling (Eds.)
260 Ionosphere Dynamics and Applications
Chaosong Huang and Gang Lu (Eds.)
261 Upper Atmosphere Dynamics and Energetics
Wenbin Wang and Yongliang Zhang (Eds.)
262 Space Weather Effects and Applications
Anthea J. Coster, Philip J. Erickson, and Louis J. Lanzerotti (Eds.)
263 Mantle Convection and Surface Expressions
Hauke Marquardt, Maxim Ballmer, Sanne Cottaar, and Jasper Konter (Eds.)
264 Crustal Magmatic System Evolution: Anatomy, Architecture, and Physico‐Chemical Processes
Matteo Masotta, Christoph Beier, and Silvio Mollo (Eds.)
Geophysical Monograph 265
Global Drought and Flood
Observation, Modeling, and Prediction
Huan WuDennis P. LettenmaierQiuhong TangPhilip J. Ward
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LIST OF CONTRIBUTORS
Robert F. AdlerNASA Goddard Space Flight CenterGreenbelt, Maryland, USA
Amir AghaKouchakDepartment of Civil and Environmental Engineering, and Department of Earth System ScienceUniversity of California IrvineIrvine, California, USA
Ali AhmadalipourCenter for Complex Hydrosystems ResearchDepartment of Civil, Construction, and Environmental EngineeringThe University of AlabamaTuscaloosa, Alabama, USA
Lorenzo AlfieriDisaster Risk Management UnitEuropean Commission Joint Research CentreIspra, Italy; andCIMA Research FoundationSavona, Italy
Martha AndersonAgricultural Research ServiceUnited States Department of AgricultureBeltsville, Maryland, USA
Mark BernhofenSchool of Civil EngineeringUniversity of LeedsLeeds, United Kingdom
Robert G. BrakenridgeInstitute of Arctic and Alpine ResearchUniversity of ColoradoBoulder, Colorado, USA
Mélody BraunRed Cross Red Crescent Climate CentreThe Hague, The Netherlands
Weitian ChenGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and School of Atmospheric SciencesSun Yat‐sen UniversityGuangdong, China
Sagy CohenDepartment of GeographyThe University of AlabamaTuscaloosa, Alabama, USA
Simon J. DadsonSchool of Geography and the EnvironmentOxford UniversityOxford, United Kingdom
Francesco DottoriDisaster Risk Management UnitEuropean Commission Joint Research CentreIspra, Italy
Colin S. DoyleCloud to StreetNew York, New York, USA; andDepartment of Geography and the EnvironmentThe University of TexasAustin, Texas, USA
Li FangNOAA NESDIS Center for Satellite Applications and ResearchCollege Park, Maryland, USA; andUMD‐CISESS Cooperative Institute for Satellite Earth System StudiesCollege Park, Maryland, USA
Min FengNational Tibetan Plateau Data CenterInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing, China; and University of Chinese Academy SciencesBeijing, China
Zachary FlamigCenter for Data Intensive ScienceUniversity of ChicagoChicago, Illinois, USA
John GalantowiczAtmospheric and Environmental Research Inc.Lexington, Massachusetts, USA
Huilin GaoZachry Department of Civil and Environmental EngineeringTexas A&M UniversityCollege Station, Texas, USA
Mitchell D. GoldbergNational Environmental Satellite, Data, and Information ServiceNational Oceanic and Atmospheric AdministrationCollege Park, Maryland, USA
Helen GreatrexInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia University,Palisades, New York, USA
Tom de GroeveDisaster Risk Management UnitEuropean Commission Joint Research CentreIspra, Italy
Christopher HainNASA Marshall Space Flight CenterHuntsville, Alabama, USA
Zengchao HaoCollege of Water SciencesBeijing Normal UniversityBeijing, China
Haixia HeNational Disaster Reduction Center of ChinaMinistry of Emergency Management of the People’s Republic of ChinaBeijing, China
Feyera A. HirpaSchool of Geography and the EnvironmentOxford UniversityOxford, United Kingdom
Jannis HochDepartment of Physical GeographyUtrecht UniversityUtrecht, The Netherlands; andDeltaresDelft, The Netherlands
Laura Hoffman‐HernandezInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia UniversityPalisades, New York, USA
Matt HorrittSchool of Civil EngineeringUniversity of LeedsLeeds, United Kingdom
Zequn HuangGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and School of Atmospheric SciencesSun Yat‐sen UniversityGuangdong, China
Saiful A.K.M. IslamInstitute of Water and Flood ManagementBangladesh University of Engineering and TechnologyDhaka, Bangladesh
Satya KalluriRaytheonUpper Marlboro, Maryland, USA
Albert KettnerInstitute of Arctic and Alpine ResearchUniversity of ColoradoBoulder, Colorado, USA
John KimballLawrence Berkeley National LaboratoryBerkeley, California, USA; andUniversity of WashingtonSeattle, Washington, USA
Andrew KruczkiewiczInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia UniversityPalisades, New York, USA; andRed Cross Red Crescent Climate CentreThe Hague, The Netherlands
William KustasAgricultural Research ServiceUnited States Department of AgricultureBeltsville, Maryland, USA
Dennis P. LettenmaierUniversity of California, Los AngelesLos Angeles, California, USA
Chaoqun LiGuangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and School of Atmospheric SciencesSun Yat‐sen UniversityGuangdong, China
Hongyi LiDepartment of Civil and Environmental EngineeringUniversity of HoustonHouston, Texas, USA
Sanmei LiDepartment of Geography and Geo‐Information ScienceGeorge Mason UniversityFairfax, Virginia, USA
Yao LiZachry Department of Civil and Environmental EngineeringTexas A&M UniversityCollege Station, Texas, USA
Dan LindseyNational Environmental Satellite, Data, and Information ServiceNational Oceanic and Atmospheric AdministrationFort Collins, Colorado, USA
Jicheng LiuNOAA NESDIS Center for Satellite Applications and ResearchCollege Park, Maryland, USA; andUMD‐CISESS Cooperative Institute for Satellite Earth System StudiesCollege Park, Maryland, USA
Brian LlamanzaresInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia UniversityPalisades, New York, USA
Valerio LoriniDisaster Risk Management UnitEuropean Commission Joint Research CentreIspra, Italy
Lifeng LuoDepartment of Geography, Environment, and Spatial SciencesMichigan State UniversityEast Lansing, Michigan, USA
Feng MaSchool of Hydrology and Water ResourcesNanjing University of Information Science and TechnologyNanjing, China; andDepartment of Geography, Environment, and Spatial SciencesMichigan State UniversityEast Lansing, Michigan, USA
David MarechalGuy Carpenter & Company GmbHMunich, Germany
Patrick MatgenLuxembourg Institute of Science and TechnologyEsch‐sur‐Alzette, Luxembourg
Shanna McClainEarth Sciences DivisionNational Aeronautics and Space AdministrationWashington, DC, USA
Hamid MoradkhaniCenter for Complex Hydrosystems ResearchDepartment of Civil, Construction, and Environmental EngineeringThe University of AlabamaTuscaloosa, Alabama, USA
Mahdi NavariNASA Goddard Space Flight CenterGreenbelt, Maryland, USA
Jeffrey C. NealSchool of Geographical SciencesUniversity of BristolBristol, United Kingdom
Miriam NielsenInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia UniversityPalisades, New York, USA
Lace PadillaDepartment of PsychologyNorthwestern UniversityEvanston, Illinois, USA
Erin Coughlan de PerezInternational Research Institute for Climate and SocietyThe Earth Institute, Columbia UniversityPalisades, New York, USA; andRed Cross Red Crescent Climate CentreThe Hague, The Netherlands; andVrije Universiteit AmsterdamAmsterdam, The Netherlands
Christel PrudhommeEuropean Centre for Medium‐range Weather ForecastsReading, United Kingdom; andCentre for Ecology and HydrologyWallingford, United Kingdom; andDepartment of Geography and EnvironmentLoughborough UniversityLoughborough, United Kingdom
Arash Modarresi RadDepartment of ComputingBoise State UniversityBoise, Idaho, USA
Lauro RossiCIMA Research FoundationSavona, Italy
Roberto RudariCIMA Research FoundationSavona, Italy
Mojtaba SadeghDepartment of Civil EngineeringBoise State UniversityBoise, Idaho, USA
Peter SalamonDisaster Risk Management UnitEuropean Commission Joint Research CentreIspra, Italy
Chris SampsonFathom GlobalBristol, United Kingdom
Mitchell SchullNOAA NESDIS Center for Satellite Applications and ResearchCollege Park, Maryland, USA; andUMD‐CISESS Cooperative Institute for Satellite Earth System StudiesCollege Park, Maryland, USA
Guy J.‐P. SchumannSchool of Geographical SciencesUniversity of BristolBristol, United Kingdom; andInstitute of Arctic and Alpine ResearchUniversity of ColoradoBoulder, Colorado, USA
Kara SiahaanInternational Federation of Red Cross and Red Crescent SocietiesGeneva, Switzerland
Andy SmithFathom GlobalBristol, United Kingdom
Jonathan A. SullivanSchool for Environment and SustainabilityUniversity of MichiganAnn Arbor, Michigan, USA
Donglian SunDepartment of Geography and Geo‐Information ScienceGeorge Mason UniversityFairfax, Virginia, USA
Qiuhong TangInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing, China
Jing TaoEarth System Science Interdisciplinary CenterUniversity of MarylandCollege Park, Maryland, USA; andLawrence Berkeley National LaboratoryBerkeley, California, USA; andDepartment of Civil and Environmental EngineeringUniversity of WashingtonSeattle, Washington, USA
Beth TellmanCloud to StreetNew York, New York, USA; andThe Earth Institute, Columbia UniversityPalisades, New York, USA
Mark A. TriggSchool of Civil EngineeringUniversity of LeedsLeeds, United Kingdom
Philip J. WardInstitute for Environmental StudiesVrije Universiteit AmsterdamAmsterdam, The Netherlands
Albrecht WeertsDeltaresDelft, The Netherlands; andWageningen University and Research CentreWageningen, The Netherlands
Huan WuSchool of Atmospheric SciencesSun Yat‐sen UniversityGuangdong, China; andSouthern Marine Science and Engineering LaboratoryGuangdong, China; andEarth System Science Interdisciplinary CenterUniversity of MarylandCollege Park, Maryland, USA
Dai YamazakiInstitute of Industrial ScienceThe University of TokyoTokyo, Japan
Hongxiang YanPacific Northwest National Laboratory U. S. Department of Energy Richland, Washington, USA
Siquan YangDivision of Science and InformatizationMinistry of Emergency Management of the People’s Republic of ChinaBeijing, China
Aizhong YeState Key Laboratory of Earth Surface and Ecological ResourcesFaculty of Geographical ScienceBeijing Normal UniversityBeijing, China
Jifu YinNOAA NESDIS Center for Satellite Applications and ResearchCollege Park, Maryland, USA; andUMD‐CISESS Cooperative Institute for Satellite Earth System StudiesCollege Park, Maryland, USA
Mahkameh ZarekariziCenter for Complex Hydrosystems ResearchDepartment of Civil, Construction, and Environmental EngineeringThe University of AlabamaTuscaloosa, Alabama, USA
Xiwu ZhanNOAA NESDIS Center for Satellite Applications and ResearchCollege Park, Maryland, USA
Shuai ZhangDepartment of Geological SciencesUniversity of North Carolina at Chapel HillChapel Hill, North Carolina, USA
Fang ZhaoKey Laboratory of Geographic Information Science (Ministry of Education)School of Geographic SciencesEast China Normal UniversityShanghai, China; andPotsdam Institute for Climate Impact ResearchPotsdam, Germany
Gang ZhaoZachry Department of Civil and Environmental EngineeringTexas A&M UniversityCollege Station, Texas, USA
Ervin ZsoterEuropean Centre for Medium‐range Weather ForecastsReading, United Kingdom
PREFACE
The increase in the frequency of drought and flood events due to changes in the global water and energy cycle poses higher risks to human settlements, especially those in floodplains and areas with frequent heat waves and deficit in precipitation, in an era of rapid population growth. Monitoring and forecasting of the occurrence, intensity, and evolution of drought and flood events are considered to be more and more important by humanitarian and government agencies for issuing timely warnings, monitoring ongoing hazards, and developing short‐term and long‐term risk assessments and management plans. In the past two decades, there have been significant advances in both numerical modeling and remote sensing approaches. These complementary approaches have been critical components in producing integrated information for droughts and floods.
This monograph reviews recent advances in the modeling and remote sensing of droughts and floods, covering many relevant topics including: (a) the currently available, widely used techniques and products for obtaining timely and accurate global‐scale or continental‐scale drought and flood information; (b) the features, strengths and weaknesses, and advances and challenges in each of these global products; (c) how these products have been used by humanitarian, government, and development sectors in recent natural disaster cases; and (d) discussions about the gaps between the products and end users, and insights for further improving the workflow in response activities from perspectives of both hazard information providers and users.
This book is organized into three closely connected sections. Part I focuses on remote sensing approaches for global drought and flood mapping. It starts with an overview of progress, challenges, and opportunities in remote sensing of drought. As critical components for drought monitoring, two well recognized remote‐sensing‐based products for evapotranspiration measurement and reservoir parameters (elevation, storage, and area) are then introduced and discussed in the following two chapters. Two widely used remote‐sensing‐based flood mapping products are described in the next two chapters, respectively, followed by a thoughtful chapter proposing an integration of Earth Observation (EO) data and numerical models, with the latter as the focus of the next section.
Part II summarizes current widely used modeling approaches and systems, including model physics, features, validation, strength, limitations, and challenges in their further improvement and applications. In this section, the first three chapters are focused on modeling of drought using statistical, process‐based or hybrid approaches. For flood modeling, an overview of the state‐of‐the‐art flood models is presented in a dedicated chapter. An open challenge for almost all global flood models, i.e., large‐scale calibration of models, is discussed in the following chapter. The rest of the section then focuses on two common data sets, i.e., derivations based on digital elevations model (DEM), and land use and land cover (LULC), which are fundamental for both drought and flood simulations.
Part III provides a review of recent advances in drought and flood damage estimation and risk assessment, and in‐depth discussions on challenges in humanitarian response and management activities when integrating the hazard information from multiple products and data sources. Flood risk assessment under climate change is first introduced and discussed. Then practical activities in hazard response from national and international agencies are detailed in the next two chapters. The final chapter of this section describes the emerging role of the Global Flood Partnership (GFP), a network of scientists, users, and private and public organizations active in global flood response and risk management. The GFP shares flood information in near real‐time for national environmental agencies and humanitarian organizations to support emergency operations and to reduce the overall socioeconomic impacts of disasters. A conclusion summarizes the whole book, with a brief discussion on existing challenges and the strategies of improving the monitoring and prediction of drought and flood.
Drought and floods have unsurprisingly become the hot topics of several recently published books. The uniqueness of this book, however, lies in the fact that: (a) it represents most of the ongoing modeling efforts, including current widely used products, and as chapter contributors are the developers of these products, this allows them to describe in detail and depth the strengths, weaknesses, advances and challenges in their further development and integration; (b) it brings together contributors from humanitarian, government, and development sectors, describing how these products are used in risk assessment and catastrophe response activities from a users’ standpoint, shedding light on how to narrow the gap between product providers and users in both expectation and communication. As a result, this book should appeal to a broad community of researchers, engineers, practitioners, policy makers, and decision makers, from various national and international agencies and nongovernmental organizations (NGOs) working in drought and flood disaster management, and in sustainable and resilient construction. It should also be of interest to college students and teachers with interests in subjects including hydrology, remote sensing, meteorology, natural hazards, emergency management, and global change.
Last, we note that many of the chapters on floods are born out of presentations given at recent American Geophysical Union’s Fall Meeting sessions on “Global Floods: Forecasting, Monitoring, Risk Assessment, and Socioeconomic Response” and the annual meetings of the Global Flood Partnership (GFP). These sessions and meetings foster global flood forecasting, monitoring, and impact assessment efforts with the aim to strengthen preparedness and response and to reduce global flood losses.
Huan WuSun Yat‐sen University, China
Dennis P. LettenmaierUniversity of California, Los Angeles, USA
Qiuhong TangChinese Academy of Sciences, China
Philip J. WardVrije Universiteit Amsterdam, The Netherlands
Part IRemote Sensing for Global Drought and Flood Observations
1Progress, Challenges, and Opportunities in Remote Sensing of Drought
Arash Modarresi Rad1, Amir AghaKouchak2, Mahdi Navari3, and Mojtaba Sadegh4
1 Department of Computing, Boise State University, Boise, Idaho, USA
2 Department of Civil and Environmental Engineering, and Department of Earth System Science, University of California Irvine, Irvine, California, USA
3 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
4 Department of Civil Engineering, Boise State University, Boise, Idaho, USA
ABSTRACT
Drought, one of the most daunting natural hazards, is linked to other hazards such as heatwaves and wildfires, and is related to global and regional food security. Given the severe environmental and socioeconomic ramifications of droughts, comprehensive and timely analysis of droughts’ onset, development, and recovery at proper spatial and temporal scales is of paramount importance. Droughts are categorized by different variables, such as precipitation, soil moisture, and streamflow, depending on the target of the analysis. The root cause of droughts, however, is sustained below‐average precipitation. Large‐scale oceanic and atmospheric circulations drive precipitation variability, and hence droughts should be analyzed from a continental to global perspective. Given the spatial scale of interest, as well as the poor spatial resolution and temporal inconsistency of ground observations, multisensor remotely sensed climatological, hydrological, and biophysical variables offer a unique opportunity to model droughts from different perspectives (meteorological, agricultural, hydrological, and socioeconomic) and at the global scale. It is also often required to model droughts using multiple indices and analyze feedbacks between droughts and other hazards, such as heatwaves. Multiple satellites, missions, and sensors offer invaluable information for multi‐indicator modeling of droughts and their feedbacks with other natural hazards in an era of big data. Remote sensing satellite data, however, are associated with major challenges including temporal limitations, consistency within and between multiple sensors and data sets, reliability, lack of uncertainty assessment, managing data volumes, and paucity of research on translating remote sensing of drought into actionable science. With challenge comes opportunity. The focus of the scientific community should be on merging the information provided from different satellites and sensors, to underpin their uncertainties, and to offer long‐term and consistent data sets for drought analysis.
1.1. INTRODUCTION
Drought is a recurring natural feature of climate and is defined as below‐normal precipitation, usually over an extended period of time (Wilhite & Buchanan‐Smith, 2005). Droughts cause billions of dollars of damage to multiple sectors globally, specifically to agriculture. Droughts may also cause, or co‐occur with, other hazards such as heatwaves, which collectively escalate the ramifications of this natural hazard (Raei et al. 2018). Indeed, the concurrence of climatic extremes, in particular droughts and heat waves, can result in forest fires (Goulden, 2018; Silva et al., 2018; Taufik et al., 2017), land degradation and desertification (Hutchinson & Herrmann, 2016; Olagunju, 2015; Vicente‐Serrano et al., 2015), water shortage for agriculture and urban water supply (AghaKouchak, Farahmand, et al., 2015; Gober et al., 2016; Khorshidi et al., 2019; Van Loon et al., 2016), and economic impacts, and may prompt water bankruptcy (Howitt et al., 2014; Madani et al., 2016). Therefore, the impacts of drought are complex and can propagate to regions outside the area of its occurrence. Drought is often categorized in four groups: meteorological, agricultural, hydrological, and socioeconomic (Dracup et al., 1980). Meteorological drought is defined as precipitation deficiency over a long period, and it best represents the onset of drought (Utah Division of Water Resources, 2007). An extended period of meteorological drought results in soil moisture deficit as evapotranspiration continues despite the lack of precipitation, which leads to agricultural drought (Cunha et al., 2015). Persistence of metrological drought ultimately reduces overall water supply and drought is manifested in a hydrological form (Modaresi Rad et al., 2016). Socioeconomic drought then occurs as supply and demand of some economic goods are impacted by meteorological, agricultural, and hydrological droughts (Shiferaw et al., 2014). The observed changes in temporal patterns of precipitation associated with unsustainable water withdrawal may escalate the drought severity around the globe (Mallakpour et al., 2018; U.S. Global Change Research Program, 2018); and large‐scale changes in weather patterns are likely to affect water storage around the globe and threaten water supply particularly in arid and semi‐arid regions (Ault et al., 2014).
Drought detection requires observation of a plethora of different climatic and biophysical variables. Observations in situ, however, do not provide a uniform spatial distribution and are limited to populated areas, hence satellite‐based observations provide a unique way to analyze and monitor drought at a global scale. Satellites offer observations for a wide range of climate variables such as precipitation, soil moisture, temperature, relative humidity, evapotranspiration, vegetation greenness, land‐cover condition, and water storage (Aghakouchak, Farahmand, et al., 2015; R. G. Allen et al., 2007; L. Wang & Qu, 2009; Whitcraft et al., 2015). Although remote sensing provides more opportunities for the scientific community to monitor Earth systems and offer better understanding of drought impact at regional to global scales, it is not without flaws or challenges. The main challenge is the insufficient length of the observed records provided for the variables of interest. Other challenges include data consistency, ease of access, quantifying uncertainty, and development of appropriate drought indices, which will be discussed throughout this chapter.
1.2. PROGRESS IN REMOTE SENSING OF DRIVERS OF DROUGHT
This section presents the recent remote sensing techniques used for identification and quantification of drought as characterized by different climatic and biophysical variables.
1.2.1. Precipitation
A meteorological drought can be described as precipitation deficiency over a period of time (WMO, 1975), often represented in terms of an index of deviation from normal. Drought indices not only serve the scientific communities but they are also great tools for facilitating the decision‐making and policy‐making processes for stakeholders and managers when compared with the raw data. One of the most widely used and informative meteorological drought indices is the standardized precipitation index (SPI) developed by Mckee et al. (1993). Several other meteorological drought indices have also been proposed, including, but not limited to, precipitation effectiveness (Thornthwaite, 1931), antecedent precipitation (API; McQuigg, 1954), rainfall anomaly (RAI; Van Rooy, 1965), drought area (Bhalme & Mooley, 1980), effective precipitation (Byun & Wilhite, 1999), and rainfall variability indices (Oguntunde et al., 2011). The SPI is currently being used in many national operational and research centers and was recognized as a global measure to characterize meteorological drought by the World Meteorological Organization (WMO, 2009). Computation of SPI requires measured rainfall data and a normalization process of monthly data, either by utilizing an appropriate probability distribution function (PDF) to transform the rainfall PDF (e.g., gamma or Pearson type III probability distribution) into a standard normal distribution (Khalili et al., 2011), or by utilizing a nonparametric approach (Hao & AghaKouchak, 2014). Precipitation deficit can be specified for different timescales (e.g., from 1 to 24 months) when using SPI, where precipitation abnormalities in shorter timescales reflect soil moisture wet/dry conditions and longer timescales portray the wet/dry conditions of subsequent processes such as streamflow, reservoir levels, and ultimately groundwater.
Since the root cause of droughts is deficit in precipitation, meteorological drought indices, and in particular SPI, are suitable indices for revealing the onset of drought (Hao & Aghakouchak, 2013). Indeed, precipitation is regarded as a key component in drought analysis. Clustering approaches have been used as a common practice to identify spatially homogeneous drought areas by utilizing meteorological drought indices such as SPI (Santos et al., 2010). Assessment of temporal variability of metrological drought utilizing SPI, however, has shown formation of noncoherent clusters in spatiotemporal clustering (Modaresi Rad & Khalili, 2015). This is due to precipitation’s large spatial variability, which creates diverse spatial patterns even at small scales. Considering spatial variability of precipitation is crucial, since a dense and evenly distributed network of gauging stations is required for describing spatiotemporal characteristics of drought. Similarly, ground‐based weather radars also suffer from spatial discontinuity and are error prone due to contamination by surface backscatter, uncertainty of approximation of relation between reflectivity and rain rate, and bright band effects, making them unfeasible for global applications (Kidd et al., 2012; Wolff & Fisher, 2008). As a result, a more robust approach would be to use satellite observations that would produce gridded data as an input not only for drought models, but also for meteorological and hydrological models such as weather research and forecasting (WRF) and variable infiltration capacity (VIC).
Figure 1.1 Rainfall map by NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite. (a) Average rate of rainfall per day for the period of 1998‐2011. (b) A tropical storm in southeast Texas causing record‐breaking floods, produced using the IMERG precipitation product.
(Courtesy: NASA’s Earth observatory: https://earthobservatory.nasa.gov/images)
Visible (VIS) satellite images provide information about cloud thickness and infrared (IR) images provide information on cloud top temperature and cloud height that are used to estimate precipitation rate via different retrieval algorithms (Joyce & Arkin, 1997; Sapiano & Arkin, 2009; Turk et al., 1999). Geostationary (GEO) VIS/IR satellites offer approximately a 15–30 min frequency of observations, but their accuracies are disputed. On the other hand, passive microwave (MW) sensors capture data of hydrometeor signals and scattering signals of raindrops, snow, and ice contents in the lower atmosphere and sense the bulk emission from liquid water, and therefore provide a more accurate estimation of precipitation rate (Behrangi et al., 2014). The MW sensors, however, often face difficulties distinguishing between light rain and clouds and have less frequent overpass (almost two observations per a day). Therefore, it is suggested that a combination of both MW and VIS/IR satellite observations can result in more accurate estimations (Joyce et al., 2004). Currently, a variety of precipitation satellite data sets or products exist, amongst which that of the Tropical Rainfall Measuring Mission (TRMM) has found notable success towards improving the forecast of extreme events (Figure 1.1a). This data set is a joint mission between the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) that advances the understanding of tropical rainfalls over the ocean by providing three‐dimensional images. The mission was launched in 1997 and terminated in 2015, and the project was continued in 2014 by NASA's Goddard Space Flight Center and JAXA as Global Precipitation Measurement (GPM), with a new calibration standard for the rest of the satellite constellation and a core observatory that possessed a Dual‐frequency Precipitation Radar (DPR) and a GPM Microwave Imager (GMI) (Hou et al., 2014). Other satellite precipitation data sets include the Climate Predicting Center (CPC) Morphing Technique (CMORPH; Joyce et al., 2004), CPC Merged Analysis of Precipitation (CMAP; Xie & Arkin, 1997), TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al., 2007), Special Sensor Microwave Imager (SSM/I; Ferraro, 1997), Global Precipitation Climatology Project (GPCP; Adler et al., 2003), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN; Figure 1.2; Ashouri et al., 2015; Hsu et al., 1997; S. Sorooshian et al., 2000), and the new GPM mission known as the Integrated MultisatellitE Retrievals for GPM (IMERG; Figure 1.1b; Huffman et al., 2015).
Figure 1.2 Near real‐time 0.04° precipitation information provided by the Global Water and Development Information (G‐WADI) map server of University California at Irvine using the PERSIANN‐Cloud Classification System (PERSIANN‐CCS).
One of the major challenges associated with satellite precipitation data is measurement or inference uncertainty due to the presence of uncorrected biases (A. Sorooshian et al., 2008). Studies have shown that although TMPA can be used to produce reliable results when driving hydrological models for monthly streamflow simulation, it does not perform well at the daily timescale (Meng et al., 2014). Since precipitation is a key variable in hydrology, the problem with uncertainty is further aggravated if it is left untreated in drought monitoring and hydrological modeling. As a result, several post‐processing techniques have been developed for bias correction (Khajehei et al., 2018; Madadgar & Moradkhani, 2014). For further information regarding the validation process against ground‐based measurements, interested reader is referred to AghaKouchak et al. (2012), Lu et al. (2018), Mateus et al. (2016), Nasrollahi et al. (2013), Y. Tian et al. (2009), and Xu et al. (2017). Another limitation of satellite precipitation data is associated with their short length of record. Drought analysis requires at least a minimum of 30 years of data (Mckee et al., 1993). Therefore, the near‐real‐time satellite precipitation products such as GPCP with nearly 19 years of recorded data cannot single‐handedly be used to develop drought‐monitoring systems. To remedy this shortcoming, near‐real‐time satellite data are combined with the long‐term GPCP to produce the required timespan for drought calculation (AghaKouchak & Nakhjiri, 2012). In their study, AghaKouchak and Nakhjiri (2012) used a merged product of GPCP (1979–2009) and PERSIANN (2010 to the present) in a Bayesian data‐merging framework to produce a near‐real‐time meteorological drought monitoring system using SPI.
1.2.2. Soil Moisture
Agricultural drought is a result of precipitation deficit plus accumulated evapotranspiration over a prolonged period of time that eventually leads to extended periods of low soil moisture that affect crop yields and livestock production (Cunha et al., 2015). Agricultural drought disrupts the chain of supply and demand of agricultural products and contributes to socioeconomic drought (Wilhite & Glantz, 1985). Soil moisture is a key component of agricultural drought and defines the readily available water that plants can access from the soil through their root system. Soil moisture regulates the water and energy exchange between the land surface and the atmosphere. It also influences the partitioning of nonintercepted precipitation into surface runoff and infiltrations and influences the partitioning of net radiation into sensible, latent, and ground heat fluxes that are essential climate variables (WMO, 2006). Soil moisture condition directly reflects ecosystem functionality and agricultural productivity, therefore an agricultural drought influences the economy at local to global scales (IPCC, 2007; Ryu et al., 2014).
Warm surface temperature and rapidly decreasing soil moisture due to a lack of precipitation and hot temperatures are associated with rapidly developing drought conditions that are often known as “flash droughts” (M. C. Anderson et al., 2013; Otkin et al., 2016). Ford et al. (2015) demonstrated that measurements of soil moisture in situ would drastically enhance the identification of flash droughts. Therefore, identification and quantification of drought at different timescales with high‐resolution satellite imagery is crucial for decision making and developing drought mitigation strategies (D’Odorico et al., 2010). Several drought indices have been proposed to address deficiency in soil moisture, including the Crop Moisture Index (CMI; Palmer, 1965), Keetch–Byram Drought Index (KBDI; Keetch & Byram, 1968), Soil Moisture Percentile (Sheffield et al., 2004), Soil Moisture Deficit Index (SMDI; Narasimhan & Srinivasan, 2005), Scaled Drought Condition Index (SDCI) that uses multisensor data (Rhee et al., 2010), Microwave Integrated Drought Index (MIDI) that integrates precipitation, soil moisture, and land surface temperature derived from microwave sensors such as TRMM and AMSR‐E (Zhang & Jia, 2013), Soil Moisture Drought Index (SODI; Sohrabi et al., 2015), and Standardized Soil Moisture Index (SSI; Hao & Aghakouchak, 2013; Figure 1.3).
Figure 1.3
