Gebremichael / Hossain Satellite Rainfall Applications for Surface Hydrology

1. Auflage 2009
ISBN: 978-90-481-2915-7
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 327 Seiten

With contributions from a panel of researchers from a wide range of fields, the chapters of this book focus on evaluating the potential, utility and application of high resolution satellite precipitation products in relation to surface hydrology.

Dr. Mekonnen Gebremichael is Assistant Professor of Civil and Environmental Engineering at the University of Connecticut. He has extensively published in the field of satellite remote sensing of hydrology. His research projects are funded by NASA, NSF and USAID, among others. Dr. Gebremichael is the recipient of awards including NASA's New Investigator Program. Dr. Faisal Hossain is Associate Professor of Water Resources in the Civil Engineering department of Tennessee Technological University. He has over 50 peer-reviewed publications in the fields of groundwater contamination mapping, flood prediction, satellite precipitation, transboundary water resources issues and engineering education. He is the recipient of numerous awards and recognitions such as NASA New Investigator Program, American Society of Engineering Education Outstanding New Faculty Research Award and Top Performer Rating by NSF Alan T Waterman Award Committee.

Autoren/Hrsg.

Gebremichael, Mekonnen

Hossain, Faisal

Inhaltsverzeichnis

1;Preface;4
2;Contents;7
3;Contributors;9
4;Part I Evolution of High Resolution Precipitation Products;12
4.1;The TRMM Multi-Satellite Precipitation Analysis (TMPA);13
4.1.1;1 Introduction;13
4.1.2;2 Instruments and Input Datasets;16
4.1.3;3 General Methodology;18
4.1.3.1;3.1 Combined Microwave Estimates;18
4.1.3.2;3.2 Microwave-Calibrated IR Estimates;20
4.1.3.3;3.3 Merged Microwave and IR Estimates;21
4.1.3.4;3.4 Rescaling to Monthly Data;21
4.1.3.5;3.5 RT Algorithm Adjustments;21
4.1.4;4 Current Status on Algorithm Development;22
4.1.5;5 Comparisons and Examples;23
4.1.5.1;5.1 Prior Results;23
4.1.5.2;5.2 Climatological Calibration of the RT;25
4.1.6;6 Future Plans/Conclusions;27
4.1.7;References;30
4.2;CMORPH: A Morphing Approach for High Resolution Precipitation Product Generation;33
4.2.1;1 Introduction;33
4.2.2;2 Description of the CMORPH Data and Methodology;34
4.2.2.1;2.1 Infrared Data;34
4.2.2.2;2.2 Passive Microwave Data;35
4.2.2.3;2.3 Rainfall Mapping;36
4.2.2.4;2.4 Intercalibration of the Various PMW-Derived Precipitation Estimates;37
4.2.2.5;2.5 CMORPH Methodology;38
4.2.3;3 Applications;40
4.2.4;4 CMORPH Improvements;43
4.2.4.1;4.1 Backward Extension and Reprocessing;43
4.2.4.2;4.2 Backward Extension of the CMORPH Period of Record;43
4.2.4.3;4.3 Kalman Filter;43
4.2.4.4;4.4 Bias Reduction;44
4.2.5;5 CMORPH Data Availability and Performance;44
4.2.6;References;46
4.3;The Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) for High-Resolution, Low-Latency Satellite-Based Rainfall Estimates;48
4.3.1;1 Introduction;48
4.3.2;2 Instruments and Input Datasets;49
4.3.3;3 General Methodology;50
4.3.4;4 Current Status on Algorithm Development;51
4.3.5;5 Comparisons and Examples;54
4.3.6;6 Future Plans and Conclusions;55
4.3.7;References;56
4.4;Extreme Precipitation Estimation Using Satellite-Based PERSIANN-CCS Algorithm;58
4.4.1;1 Introduction;58
4.4.2;2 Methodology;59
4.4.2.1;2.1 Cloud Image Segmentation;60
4.4.2.2;2.2 Input Feature Extraction;61
4.4.2.3;2.3 Image Classification;62
4.4.2.4;2.4 Mapping Patch to Pixel Rainfall;63
4.4.3;3 Application Examples;65
4.4.3.1;3.1 Hurricane Ernesto;65
4.4.3.2;3.2 Hurricane Katrina;67
4.4.4;4 Real-Time High Resolution Global Precipitation Server;68
4.4.4.1;4.1 Map Navigation;70
4.4.4.2;4.2 Query Functions;72
4.4.4.3;4.3 Data Access;73
4.4.5;5 Conclusions and Future Directions;73
4.4.6;References;75
4.5;The Combined Passive Microwave-Infrared (PMIR) Algorithm;77
4.5.1;1 Background;77
4.5.2;2 Algorithm Description;80
4.5.2.1;2.1 Data Ingest and Preparation;80
4.5.2.2;2.2 Database Management;82
4.5.2.3;2.3 Results Generation;82
4.5.3;3 Application and Results;82
4.5.4;4 Conclusions;88
4.5.5;References;89
4.6;The NRL-Blend High Resolution Precipitation Product and its Application to Land Surface Hydrology;92
4.6.1;1 High Resolution Precipitation Products (HRPP);92
4.6.2;2 NRL-Blend HRPP Technique;93
4.6.2.1;2.1 Time-Space Colocation of LEO and GEO Datasets;94
4.6.2.2;2.2 Instantaneous Rainrate Adjustment;95
4.6.2.3;2.3 Accumulations Procedure;96
4.6.2.4;2.4 Comparisons with Numerical Weather Prediction Models;97
4.6.3;3 Ground Validation;98
4.6.3.1;3.1 Verification Efforts of the International Precipitation Working Group (IPWG);99
4.6.3.2;3.2 Satellite Omission Experiments;99
4.6.4;4 Sensitivity of Land Surface Parameters;102
4.6.5;5 Land Surface Model Response;102
4.6.5.1;5.1 Configuration of Land Surface Models;103
4.6.5.2;5.2 Soil Water Content Sensitivity;104
4.6.6;6 Conclusions;109
4.6.7;References;110
4.7;Kalman Filtering Applications for Global Satellite Mapping of Precipitation (GSMaP);112
4.7.1;1 Introduction;112
4.7.2;2 Data;114
4.7.3;3 Methodology;115
4.7.4;4 Current Status of the System;118
4.7.5;5 Comparisons and Examples;123
4.7.5.1;5.1 Example;123
4.7.5.2;5.2 Comparison and Validation;126
4.7.6;6 Future Plans and Conclusions;128
4.7.7;References;129
5;Part II Evaluation of High Resolution Precipitation Products;131
5.1;Neighborhood Verification of High Resolution Precipitation Products;132
5.1.1;1 Introduction;132
5.1.2;2 Neighborhood Verification Methods;134
5.1.3;3 Neighborhood Verification of CMORPH and TMPA Precipitation Estimates;138
5.1.4;4 Discussion;145
5.1.5;References;147
5.2;A Practical Guide to a Space-Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data;149
5.2.1;1 Introduction;149
5.2.2;2 Why SREM2D?;151
5.2.3;3 General Modeling Structure Of SREM2D;152
5.2.4;4 Formulation of SREM2D Error Metrics;154
5.2.4.1;4.1 Probabilities of Detection (Rain and No-Rain) (Metrics 1 and 2);154
5.2.4.2;4.2 False Alarm Rain Rate Distribution (Metric 3);155
5.2.4.3;4.3 Correlation Lengths (Metrics 4, 5 and 8);155
5.2.4.4;4.4 Conditional Rain Rate Distribution (Metrics 6 and 7);156
5.2.4.5;4.5 Lag-One Temporal Correlation (Metric 9);158
5.2.5;5 Data QA/QC and Calibration of Metrics for SREM2D;158
5.2.5.1;5.1 Quality Assessment and Quality Control;158
5.2.5.2;5.2 Error Metric Calibration;159
5.2.6;6 SREM2D Simulation And Reproducibility Of Error Statistics;162
5.2.6.1;6.1 Simulation Issues;163
5.2.6.2;6.2 Reproducibility of SREM2D Error Statistics;165
5.2.6.2.1;6.2.1 Checking the Consistency of Ensemble of Cumulative Hyetograph Against Actual Satellite Rainfall Data;166
5.2.6.2.2;6.2.2 Checking Reproducibility of Error Metrics;166
5.2.7;7 Conclusions;168
5.2.8;References;170
5.3;Regional Evaluation Through Independent Precipitation Measurements: USA;172
5.3.1;1 Introduction;172
5.3.2;2 Results From IPWG Daily US Validation Site;176
5.3.3;3 Sub-Daily Validation;181
5.3.4;4 Evaluation of Warm Season US Precipitation Using Gauges From NAME;184
5.3.5;5 Discussion;191
5.3.6;References;193
5.4;Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America;195
5.4.1;1 Introduction;195
5.4.2;2 Study Regions and Data;196
5.4.2.1;2.1 Study Region;197
5.4.2.2;2.2 Gauge Data;199
5.4.2.3;2.3 Satellite Data;200
5.4.3;3 Comparison of the Satellite Rainfall Products;201
5.4.4;4 Conclusion;204
5.4.5;References;205
5.5;Evaluation Through Independent Measurements: Complex Terrain and Humid Tropical Region in Ethiopia;207
5.5.1;1 Introduction;207
5.5.2;2 Data and Methods;208
5.5.2.1;2.1 Study Region;208
5.5.2.2;2.2 Types of High-Resolution Satellite Products Used;209
5.5.2.3;2.3 Rainfall Field Experiment;209
5.5.2.4;2.4 Method of Analysis;210
5.5.3;3 Results and Discussion;210
5.5.4;4 Conclusions;215
5.5.5;References;216
5.6;Error Propagation of Satellite-Rainfall in Flood Prediction Applications over Complex Terrain: A Case Study in Northeastern Italy;217
5.6.1;1 Introduction;217
5.6.2;2 Methodology;219
5.6.2.1;2.1 Study Area and Data;219
5.6.2.2;2.2 Satellite-Rainfall Ensembles;221
5.6.2.3;2.3 Hydrologic Simulations;221
5.6.3;3 Results;224
5.6.4;4 Conclusions;226
5.6.5;References;228
5.7;Probabilistic Assessment of the Satellite Rainfall Retrieval Error Translation to Hydrologic Response;230
5.7.1;1 Introduction;230
5.7.2;2 Methodology;232
5.7.3;3 Generating Satellite Precipitation Ensemble;232
5.7.4;4 Study Basin and Datasets;234
5.7.5;5 Hydrologic Model and Ensemble Streamflow Simulation;235
5.7.6;6 Results with Statistical Ensemble Verification;236
5.7.7;7 Summary and Conclusion;240
5.7.8;References;241
6;Part III Real Time Operations for Decision Support Systems;244
6.1;Applications of TRMM-Based Multi-Satellite Precipitation Estimation for Global Runoff Prediction: Prototyping a Global Flood Modeling System;245
6.1.1;1 Introduction;245
6.1.2;2 A Quasi-Global Flood Modeling Framework;247
6.1.2.1;2.1 Satellite-Based Precipitation Products;248
6.1.2.2;2.2 A Central Geospatial Database;248
6.1.2.3;2.3 A Cost-Effective Hydrological Simulation Model;249
6.1.3;3 Modified NRCS-CN Method for Global Rainfall-Runoff Simulation;250
6.1.3.1;3.1 Mapping a Spatially Distributed Global NRCS-CN;250
6.1.3.2;3.2 Time-Variant NRCS-CN;251
6.1.4;4 Implementation of the GFM;253
6.1.4.1;4.1 Retrospective Simulation;254
6.1.4.2;4.2 Implementation Interface;258
6.1.5;5 Summary and Discussion;259
6.1.5.1;5.1 Summary;260
6.1.5.2;5.2 Discussion and Directions of Alternative Flood Modeling Work;260
6.1.6;Refernces;262
6.2;Real-Time Hydrology Operations at USDA for Monitoring Global Soil Moisture and Auditing National Crop Yield Estimates;266
6.2.1;1 USDAs Global Agriculture Economic Information System;268
6.2.2;2 Operational Precipitation Products Utilized by USDA/FAS;270
6.2.2.1;2.1 Ground Station Data From the World Meteorological Organization (WMO);273
6.2.2.2;2.2 AGRMET From the Air Force Weather Agency (AFWA);273
6.2.2.3;2.3 TMPA-RT From National Aeronautics and Space Administration (NASA);274
6.2.2.4;2.4 CMORPH From National Oceanic and Atmospheric Administration (NOAA);275
6.2.2.5;2.5 NEXRAD From National Weather Service (NWS);276
6.2.2.6;2.6 Other Precipitation Data Sets (National, Regional and Commercial);276
6.2.3;3 Operational Soil Moisture Products Utilized by USDA/FAS;277
6.2.3.1;3.1 Modified Palmer Two-Layer Soil Moisture Model;279
6.2.3.2;3.2 Surface Wetness;279
6.2.4;4 Global Agriculture Monitoring (GLAM) System;282
6.2.4.1;4.1 Corrected Soil Moisture Model With Passive Microwave (PMW);283
6.2.4.2;4.2 Operational Surface Water Heights From Satellite Radar Altimetry;284
6.2.4.3;4.3 Operational Yield-Regression and Analog-Year Analysis;286
6.2.5;5 Future Outlook;289
6.2.6;References;290
6.3;Real-Time Decision Support Systems: The Famine Early Warning System Network;293
6.3.1;1 Introduction;293
6.3.1.1;1.1 The Three Components of the FEWS NET Planning Process;295
6.3.1.2;1.2 Focus on Eastern African Food Insecurity in 2009;295
6.3.2;2 Background;295
6.3.2.1;2.1 A Brief History of FEWS NET;296
6.3.2.2;2.2 The FEWS NET Early Warning System;297
6.3.2.3;2.3 A Synopsis of USGS FEWS NET Early Warning Research;298
6.3.2.4;2.4 A Synopsis of FEWS NET-Related Climate Change and Food Security Research;299
6.3.3;3 Techniques for Evaluating Hydrologic Risk;300
6.3.3.1;3.1 Low Frequency and High Frequency Models for Food Security Risk Monitoring;300
6.3.3.2;3.2 Evaluating Low Frequency Changes in Food Security Risks with Food and Water Balance Models;301
6.3.3.3;3.3 Combining Long-Term and Real-Time Satellite Rainfall Records;303
6.3.3.4;3.4 Monitoring High Frequency Shocks Using Water Requirement Satisfaction Index Maps;307
6.3.4;4 Analysis of Kenyan Agricultural Hydrologic Conditions;310
6.3.4.1;4.1 WRSI Anomalies for the 2007 and 2008 Long and Short Rains;310
6.3.4.2;4.2 The 2007 and 2008 Seasons in Historical Context;311
6.3.4.3;4.3 1979--2008 Trends in Kenyan Rainfall and WRSI;312
6.3.5;5 Summary and Discussion;315
6.3.6;References;316
6.4;Index;319