Note: Front Cover -- Satellite Soil Moisture Retrieval: Techniques and Applications -- Copyright -- Dedication -- Contents -- List of Contributors -- Author Biographies -- Preface -- Acknowledgments -- About the Cover -- Section I: Introduction -- Chapter 1: Soil Moisture from Space: Techniques and Limitations -- 1. Introduction -- 2. Means of Measuring Soil Moisture -- 2.1. Remotely Sensed Soil Moisture, The Main Approaches -- 2.2. Microwave as a Tool for Soil Moisture Monitoring: Current Status -- 3. Satellite Missions -- 4. Soil Moisture Retrieval From Space Using Passive Microwaves -- 4.1. Surface Soil Moisture -- 4.2. Root-Zone Soil Moisture -- 5. Way Forward -- 6. Caveats -- 7. Conclusions and Perspectives -- References -- Chapter 2: Available Data Sets and Satellites for Terrestrial Soil Moisture Estimation -- 1. Introduction -- 2. In Situ Data Sets for Soil Moisture -- 2.1. International Soil Moisture Network -- 2.2. Field Campaigns -- 2.2.1. Soil Moisture Experiments Series -- 2.2.1.1. Soil Moisture Experiment 2002 -- 2.2.1.2. Soil Moisture Experiment 2003 -- 2.2.1.3. Soil Moisture Experiment 2004 -- 2.2.1.4. Soil Moisture Experiment 2005 -- 2.2.2. Canadian Experiment for Soil Moisture in 2010 -- 2.2.3. Soil Moisture Active Passive Validation Experiment -- 2.2.3.1. SMAPVEX08 -- 2.2.3.2. SMAPVEX12 -- 2.2.4. Soil Moisture Active Passive Experiments -- 2.3. FLUXNET sites -- 3. Satellite Data Sets for Soil Moisture -- 3.1. The Scanning Multichannel Microwave Radiometer (SMMR) -- 3.2. Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E/2) -- 3.3. Advanced Scatterometer (ASCAT) -- 3.4. Soil Moisture and Ocean Salinity (SMOS) -- 3.5. Soil Moisture Active and Passive (SMAP) Mission -- 4. Conclusion -- References -- Section II: Optical and Infrared Techniques & -- Synergies Between them. , Chapter 3: Soil Moisture Retrievals Using Optical/TIR Methods -- 1. Introduction -- 2. Optical/TIR Model History and Concept -- 2.1. History -- 2.2. The Ts/VI Concept -- 3. Optical/TIR Models Used for SM Estimation -- 3.1. Direct Estimation of SM From Ts/VI Space -- 3.2. Models Based on Ts/VI and Empirical Equations -- 4. Case Study: Estimation of SM Using Optical/TIR RS in the Canadian Prairies -- 4.1. Introduction -- 4.2. Materials and Methods -- 4.2.1. Study Area and Data -- 4.2.2. Methodology -- 4.3. Results -- 4.3.1. Comparing EF Estimations Retrieved From Three Different Approaches -- 4.3.2. SM Estimation From Evaporative Fraction -- 4.3.3. Correlations Between Estimated SM and Field Data -- 5. Summary and Future Outlook -- References -- Chapter 4: Optical/Thermal-Based Techniques for Subsurface Soil Moisture Estimation -- 1. Introduction -- 2. Methodology -- 2.1. Study Area -- 2.2. Satellite Data, Estimation of TVDI, and Measurements -- 3. Results and Discussion -- 3.1. TVDI Parameters -- 3.2. Comparison of TVDI and Subsurface Soil Moisture Measurements -- 4. Conclusions -- References -- Chapter 5: Spatiotemporal Estimates of Surface Soil Moisture from Space Using the Ts/VI Feature Space -- 1. Introduction -- 2. The Ts/VI Domain -- 3. Experimental Set Up and Data Sets -- 3.1. CarboEurope In Situ Measurements -- 3.2. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Imagery -- 3.3. The Advanced Along Track Scanning Radiometer (AATSR) Imagery -- 3.4. The SimSphere Land Biosphere Model -- 4. Methodology -- 4.1. Preprocessing -- 4.2. ``Triangle´´ Implementation -- 4.3. Coupling EO With the SVAT Model to Retrieve SMC -- 5. Results -- 6. Discussion -- 7. Conclusions -- Acknowledgments -- References -- Chapter 6: Spatial Downscaling of Passive Microwave Data With Visible-to-Infrared Information for High-Resolution Soil Mo. , 1. Introduction -- 2. A Semiempirical Model to Capture the Synergy of Passive Microwaves With Optical Data at Different Spatial Scales -- 3. High-Resolution Soil Moisture Mapping From Space -- 3.1. Long-Term Validation Over the Central Part of the Duero Basin -- 3.2. Exploring the Use of SWIR-Based Vegetation Indices to Disaggregate SMOS Observations to 500m -- 4. Airborne Field Experiments -- 4.1. Airborne Platform for Simultaneous Thermal, VNIR Hyperspectral and Microwave L-Band Acquisions: Proof-of-Concept and... -- 4.2. Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation -- 5. Future Lines and Recommendations -- Acknowledgments -- References -- Section III: Microwave Soil Moisture Retrieval Techniques -- Chapter 7: Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach: Theory and Applications -- 1. Introduction -- 2. Radiative Transfer Equation -- 2.1. Tau-Omega Model -- 2.2. Effective Temperature, Single Dispersion and Vegetation Optical Depth -- 2.3. A Combined Optical-Passive Microwave Approach -- 2.4. Case Study-Optical Passive Microwave at in-situ level -- 2.5. Case Study-Optical Passive Microwave at Regional Scale -- 2.5.1. Study Area -- 2.5.2. Data -- 2.5.3. Results -- 2.5.4. Discussion -- 2.6. Toward a Thermal Infrared Contribution in the Optical-Passive Microwave Approach -- 3. Conclusions -- Acknowledgments -- References -- Chapter 8: Nonparametric Model for the Retrieval of Soil Moisture by Microwave Remote Sensing -- 1. Introduction -- 2. Material and Methods -- 2.1. Instrumentation Setup and Observations -- 2.2. Radial Basis Function Artificial Neural Network -- 2.3. Performance Indices -- 3. Results and Discussion -- 3.1. Assessment of Data Sets -- 3.2. Estimation of Soil Moisture Using the RBFANN -- 4. Conclusions -- References. , Chapter 9: Temperature-Dependent Spectroscopic Dielectric Model at 0.05-16 GHz for a Thawed and Frozen Alaskan Organic Soil -- 1. Introduction -- 2. Soil Samples and Measurement Procedures -- 3. Concept of a Multirelaxation Spectroscopic Dielectric Model -- 4. Retrieving the Parameters of the Multirelaxation Spectroscopic Dielectric Model -- 5. The Temperature-Dependent Multirelaxation Spectroscopic Dielectric Model -- 6. Evaluation of the TD MRSDM -- 7. Conclusions -- References -- Chapter 10: Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation -- 1. Introduction -- 1.1. Measurement Spatial Resolution -- 1.2. Soil Moisture Sensitivity and Estimation Accuracy -- 2. SR CAP Soil Moisture Retrieval -- 3. MR CAP Soil Moisture Retrieval -- 3.1. Multi-Temporal and Multi-Scale Method -- 3.2. Probabilistic and Machine Learning Techniques -- 4. Forward Electromagnetic Scattering and Emission Model Considerations -- 5. Further Discussions -- References -- Chapter 11: Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European ... -- 1. Introduction -- 2. Materials and Methods -- 2.1. In Situ Soil Moisture Observations -- 2.2. ECV Soil Moisture Observations -- 2.3. ASAR Soil Moisture Observations -- 2.4. Characterization of Errors -- 3. Results and Discussion -- 3.1. Time Series Temporal Analysis -- 3.2. Seasonal Analysis -- 4. Conclusions -- Acknowledgments -- References -- Section IV: Advanced Applications of Soil Moisture -- Chapter 12: Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy -- 1. Introduction -- 2. PRESSCA Early Warning System -- 3. Case Study and Data Sets -- 3.1. Study Area and Ground Meteorological Observations -- 3.2. Satellite Soil Moisture Observations -- 4. Results and Discussion. , 4.1. Comparison Between Satellite, In Situ, and Modeled Soil Moisture Data -- 4.2. Impact of Soil Moisture Condition on Landslide Hazard -- 5. Conclusions and Future Perspectives -- Acknowledgments -- References -- Chapter 13: Remotely Sensed Soil Moisture as a Key Variable in Wildfires Prevention Services: Towards New Prediction Tool... -- 1. Introduction -- 2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 2.1. Remote Sensing of the Earth's Soil Moisture -- 2.2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 3. The Role of Soil Moisture in Forest Fires -- 3.1. Droughts and High Temperatures Lead to Extreme Forest Fires Events -- 3.2. Dead Fuels Moisture Is a Key Variable in Forest Fire Risk Indices -- 4. Linking Remotely Sensed Soil Moisture With Forest Fires Ignition and Propagation -- 5. Fire Risk Assessment in the Iberian Peninsula Using SMOS Data -- 5.1. New Fire Risk Maps Over the Iberian Peninsula Based on SMOS Data -- 5.2. Fire Risk Maps Availability and Operational Applications -- 6. Conclusions -- Acknowledgments -- References -- Chapter 14: Integrative Use of Near-Surface Satellite Soil Moisture and Precipitation for Estimation of Improved Irrigati... -- 1. Introduction -- 2. Material and Methods -- 2.1. Study Area -- 2.2. Ground Validation Data -- 2.3. Satellite Data -- 2.4. Numerical Modeling -- 2.4.1. Initial and Boundary Conditions -- 2.4.2. Soil Parameters -- 2.5. Evaluation Criteria -- 3. Results and Discussion -- 3.1. Comparison of TRMM Rainfall With Ground-Based Rainfall Measurements -- 3.2. Soil Hydraulic Parameters -- 3.3. Soil Moisture Calibration and Validation -- 4. Conclusions -- Acknowledgments -- References -- Chapter 15: A Comparative Study on SMOS and NLDAS-2 Soil Moistures Over a Hydrological Basin-With Continental Climate -- 1. Introduction -- 2. Data and Methodology. , 2.1. Study Area and Data Sets.

Note: Front Cover -- Sensitivity Analysis in Earth Observation Modelling -- Sensitivity Analysis in Earth Observation Modelling -- Copyright -- Dedication -- Contents -- List of Contributors -- Preface -- ABOUT THE COVER -- 1 - INTRODUCTION TO SA IN EARTH OBSERVATION (EO) -- 1 - OVERVIEW OF SENSITIVITY ANALYSIS METHODS IN EARTH OBSERVATION MODELING -- 1. INTRODUCTION -- 1.1 DEFINING THE MODEL OUTPUTS AND INPUTS FOR SENSITIVITY ANALYSIS -- 1.1.1 Defining Factor (or Parametric) Uncertainty -- 2. LOCAL SENSITIVITY ANALYSIS -- 2.1 CORRELATION ANALYSIS -- 2.2 REGRESSION ANALYSIS -- 3. GLOBAL SENSITIVITY ANALYSIS -- 3.1 ONE-AT-A-TIME SENSITIVITY ANALYSIS METHODS -- 3.2 THE MORRIS METHOD FOR FACTOR SCREENING -- 3.3 VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.4 SAMPLING METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.4.1 Random Sampling -- 3.4.2 Stratified Sampling and the Latin Hypercube -- 3.4.3 Sampling for Sensitivity Indices -- 3.5 SURROGATE MODELS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.5.1 Generalized Linear Modeling -- 3.5.2 Neural Networks -- 3.5.3 Direct Sensitivity Analysis of Surrogate Models -- 3.6 POLYNOMIAL CHAOS -- 3.7 GAUSSIAN PROCESS AND BAYES LINEAR EMULATION -- 4. GRAPHICAL METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 4.1 SCATTER PLOTS -- 4.2 PLOTTING THE RESPONSE SURFACE -- 4.3 PLOTTING THE SENSITIVITY INDICES -- 5. CONCLUSIONS -- REFERENCES -- 2 - MODEL INPUT DATA UNCERTAINTY AND ITS POTENTIAL IMPACT ON SOIL PROPERTIES -- 1. INTRODUCTION -- 2. A WORLD OF MODELS - HOW CAN THEY BE CLASSIFIED? -- 3. CAN WE TRUST MODELS? - MODEL ACCURACY AND THEIR SENSITIVITY TO INPUT DATA UNCERTAINTY -- 4. SELECTING THE MOST APPROPRIATE MODEL -- 5. WHY AND HOW TO ACCOUNT FOR MODELING UNCERTAINTIES CAUSED BY DIFFERENT INPUT DATA SOURCES -- 6. ASSESSING SENSITIVITY OF ENVIRONMENTAL MODELS. , 7. HOW SOIL TEXTURE MEASURED WITH VISIBLE-NEAR-INFRARED SPECTROSCOPY AFFECTS HYDROLOGICAL MODELING: A CASE STUDY -- 7.1 STUDY SITES AND INSTRUMENTS -- 7.2 SOIL SAMPLES -- 7.3 CHEMOMETRICS -- 7.4 IMPACT OF CHEMOMETRIC METHOD ON SOIL PREDICTION -- 7.5 DIFFERENT INSTRUMENTS, DIFFERENT SOIL PREDICTIONS? WHAT WAS FINALLY THE BEST SOIL PREDICTION ACCURACY? -- 7.6 WHAT DOES THIS FINALLY MEAN FOR OUR ENVIRONMENTAL MODELING? -- 8. WHAT DID WE LEARN? -- REFERENCES -- 2 - LOCAL SA METHODS: CASE STUDIES -- 3 - LOCAL SENSITIVITY ANALYSIS OF THE LANDSOIL EROSION MODEL APPLIED TO A VIRTUAL CATCHMENT -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1 MODEL DESCRIPTION -- 2.2 SENSITIVITY ANALYSIS -- 2.2.1 Parameters -- 2.2.2 Virtual Catchment -- 2.2.3 Sensitivity Calculation -- 3. RESULTS AND DISCUSSION -- 3.1 LINEAR HILLSLOPE -- 3.1.1 Aggregated Parameters -- 3.2 COMPLEX HILLSLOPES -- 4. CONCLUDING REMARKS -- Acknowledgments -- REFERENCES -- 4 - SENSITIVITY OF VEGETATION PHENOLOGICAL PARAMETERS: FROM SATELLITE SENSORS TO SPATIAL RESOLUTION AND TEMPORAL CO ... -- 1. INTRODUCTION -- 2. MONITORING VEGETATION PHENOLOGY -- 3. SENSITIVITY ANALYSIS -- 4. SENSITIVITY OF REMOTELY SENSED PHENOLOGICAL PARAMETERS -- 4.1 SATELLITE SENSOR -- 4.2 VEGETATION INDEX -- 4.3 SPATIAL RESOLUTION -- 4.4 COMPOSITE PERIOD, SMOOTHING, AND FILTERING -- 4.4.1 Composite Period -- 4.4.2 Smoothing Techniques -- 5. CASE STUDY -- 5.1 STUDY AREA -- 5.2 DATA AND METHODOLOGY -- 5.3 RESULTS AND DISCUSSION -- 6. CONCLUSION -- REFERENCES -- 5 - RADAR RAINFALL SENSITIVITY ANALYSIS USING MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR∗ -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 STUDY AREA AND DATA SOURCES -- 2.2 THE MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR -- 2.3 THE XINANJIANG MODEL -- 3. METHODOLOGY -- 3.1 EXPERIMENTAL DESIGN -- 3.2 VERIFICATION METHOD -- 4. RESULTS AND DISCUSSION. , 4.1 IMPLEMENTATION OF ENSEMBLE FLOW GENERATION -- 4.2 IMPACT OF ERROR DISTRIBUTION ON MODEL OUTPUT -- 4.3 IMPACT OF SPATIOTEMPORAL DEPENDENCE ON MODEL OUTPUT -- 5. CONCLUSIONS -- REFERENCES -- 6 - FIELD-SCALE SENSITIVITY OF VEGETATION DISCRIMINATION TO HYPERSPECTRAL REFLECTANCE AND COUPLED STATISTICS -- 1. INTRODUCTION -- 2. BACKGROUND ON SPECTRAL DISCRIMINATION OF VEGETATION -- 2.1 PARAMETRIC VERSUS NONPARAMETRIC STATISTICAL TESTS -- 2.1.1 Other Discrimination Methods -- 2.2 UNALTERED VERSUS PROCESSED HYPERSPECTRAL REFLECTANCE -- 2.3 CASE STUDIES FOR EFFECTS OF TYPE OF REFLECTANCE AND STATISTICAL TEST ON THE VEGETATION DISCRIMINATION RESULTS -- 3. SENSITIVITY OF SPECTRAL DISCRIMINATION OF VEGETATION TO THE TYPE OF REFLECTANCE AND STATISTICAL TEST -- 3.1 HYPERSPECTRAL DATA AND METHOD DESCRIPTION -- 3.2 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO REFLECTANCE TYPE AND STATISTICAL METHOD -- 3.3 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO THE NUMBER OF OBSERVATIONS -- 4. FINAL REMARKS -- REFERENCES -- 3 - GLOBAL (OR VARIANCE)-BASED SA METHODS: CASE STUDIES -- 7 - A MULTIMETHOD GLOBAL SENSITIVITY ANALYSIS APPROACH TO SUPPORT THE CALIBRATION AND EVALUATION OF LAND SURFACE MODELS -- 1. INTRODUCTION -- 2. MODEL AND METHODS -- 2.1 REGIONAL SENSITIVITY ANALYSIS -- 2.2 VARIANCE-BASED SENSITIVITY ANALYSIS -- 2.3 THE PAWN DENSITY-BASED METHOD -- 2.4 THE JULES MODEL -- 2.5 THE SANTA RITA CREOSOTE STUDY SITE -- 2.6 EXPERIMENTAL SETUP: DEFINITION OF INPUT FACTORS AND OUTPUTS -- 2.7 DEFINITION OF THE RANGE OF VARIATION OF THE INPUT FACTORS -- 3. RESULTS -- 3.1 RESULTS OF REGIONAL SENSITIVITY ANALYSIS -- 3.2 RESULTS OF VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.3 RESULTS OF PAWN -- 3.4 OVERALL SENSITIVITY ASSESSMENT FROM THE MULTIMETHOD APPROACH -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES. , 8 - GLOBAL SENSITIVITY ANALYSIS FOR SUPPORTING HISTORY MATCHING OF GEOMECHANICAL RESERVOIR MODELS USING SATELLITE I ... -- 1. INTRODUCTION -- 2. CASE STUDY -- 2.1 SURFACE DEFORMATION AT THE KB-501 WELL OF IN SALAH SITE -- 2.2 THREE-DIMENSIONAL HYDROMECHANICAL MODEL OF KB-501 -- 3. METHODS -- 3.1 VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS -- 3.2 PRINCIPLES OF METAMODELING -- 3.3 INTRODUCTION TO KRIGING METAMODEL -- 3.4 DESCRIPTION OF THE WORKFLOW -- 4. APPLICATION -- 4.1 REDUCING THE NUMBER OF UNCERTAINTY INPUT PARAMETERS -- 4.2 CALIBRATION OF THE RESERVOIR YOUNG'S MODULUS -- SUMMARY AND FUTURE WORK -- Acknowledgments -- REFERENCES -- 9 - ARTIFICIAL NEURAL NETWORKS FOR SPECTRAL SENSITIVITY ANALYSIS TO OPTIMIZE INVERSION ALGORITHMS FOR SATELLITE-BAS ... -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 ARTIFICIAL NEURAL NETWORKS: OVERVIEW -- 2.1.1 Artificial Neural Networks for the Inversion of Satellite Measurements of Spectral Radiation for the Observation of the Ear ... -- 2.2 NEURAL NETWORK-BASED TECHNIQUES TO REDUCE THE INPUT VECTOR DIMENSIONALITY -- 2.2.1 Extended Pruning -- 2.2.2 Autoassociative Neural Networks -- 2.3 SAMPLE DATA SET -- 2.3.1 Sulfate Aerosols and Their Extinction Coefficient -- 2.3.2 Thermal Infrared Satellite Pseudo-Observations -- 3. RESULTS -- 3.1 TRAINING AND TESTING THE MAXIMUM DIMENSIONALITY NEURAL NETWORK -- 3.2 SELECTION OF THE INPUT WAVELENGTHS AND SPECTRAL SENSITIVITY ANALYSIS -- 3.3 COMPARING THE PERFORMANCES OF REDUCED DIMENSIONALITY NEURAL NETWORK -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES -- 10 - GLOBAL SENSITIVITY ANALYSIS FOR UNCERTAIN PARAMETERS, MODELS, AND SCENARIOS -- 1. INTRODUCTION -- 2. MORRIS METHOD -- 3. SOBOL' METHOD -- 3.1 FIRST-ORDER AND TOTAL-EFFECT SENSITIVITY INDICES -- 3.2 MONTE CARLO IMPLEMENTATION AND TWO APPROXIMATION METHODS. , 3.2.1 Sparse-Grid Collocation for Evaluating Mean and Variance -- 3.2.2 Distributed Evaluation of Local Sensitivity Analysis -- 4. SOBOL' METHOD FOR MULTIPLE MODELS AND SCENARIOS -- 4.1 HIERARCHICAL FRAMEWORK FOR UNCERTAINTY QUANTIFICATION -- 4.2 GLOBAL SENSITIVITY INDICES FOR SINGLE MODEL AND SINGLE SCENARIO -- 4.3 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS BUT SINGLE SCENARIO -- 4.4 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5. SYNTHETIC STUDY WITH MULTIPLE SCENARIOS AND MODELS -- 5.1 SYNTHETIC CASE OF GROUNDWATER REACTIVE TRANSPORT MODELING -- 5.2 UNCERTAIN SCENARIOS, MODELS, AND PARAMETERS -- 5.3 TOTAL SENSITIVITY INDEX FOR HEAD UNDER INDIVIDUAL MODELS AND SCENARIOS -- 5.4 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS BUT INDIVIDUAL SCENARIOS -- 5.5 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5.6 TOTAL SENSITIVITY INDEX FOR ETHENE CONCENTRATION -- 6. USING GLOBAL SENSITIVITY ANALYSIS FOR SATELLITE DATA AND MODELS -- 7. CONCLUSIONS AND PERSPECTIVES -- Acknowledgments -- REFERENCES -- 4 - OTHER SA METHODS: CASE STUDIES -- 11 - SENSITIVITY AND UNCERTAINTY ANALYSES FOR STOCHASTIC FLOOD HAZARD SIMULATION -- 1. INTRODUCTION -- 2. BASIC PRINCIPLES OF STOCHASTIC APPROACH TO FLOOD HAZARD -- 2.1 STOCHASTIC SIMULATION OF RESERVOIR INFLOWS -- 2.1.1 Storm Seasonality -- 2.1.2 Precipitation Magnitude-Frequency Relationship -- 2.1.3 Temporal and Spatial Distribution of Storms -- 2.1.4 Air Temperature and Freezing Level Temporal Patterns -- 2.1.5 The 1000-mb Air Temperature Simulation -- 2.1.6 Air Temperature Lapse Rates -- 2.1.7 Freezing Level -- 2.1.8 Watershed Model Antecedent Conditions Sampling -- 2.1.9 Initial Reservoir Level -- 2.2 SIMULATION OF RESERVOIR OPERATION-FLOOD ROUTING -- 2.3 SIMULATION PROCEDURE. , 3. UNCERTAINTY ASSOCIATED WITH STOCHASTICALLY DERIVED FLOOD QUANTILES.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Preface -- Table of Contents -- List of Contributors -- Section I: General -- 1: Introduction to Geospatial Technology for Water Resources -- Section II: Geographical Information System Based Approaches -- 2: GIS Supported Water Use Master Plan: A Planning Tool for Integrated Water Resources Management in Nepal -- 3: Spatial Integration of Rice-based Cropping Systems for Soil and Water Quality Assessment Using Geospatial Tools and Techniques -- 4: A Geographic Information System (GIS) Based Assessment of Hydropower Potential within the Upper Indus Basin Pakistan -- 5: Flood Risk Assessment for Kota Tinggi, Johor, Malaysia -- 6: Delineation and Zonation of Flood Prone Area Using Geo-hydrological Parameters: A Case Study of Lower Ghaghara River Valley -- 7: Geospatial Technology for Water Resource Development in WGKKC2 Watershed -- Section III: Satellite Based Approaches -- 8: Predicting Flood-vulnerability of Areas Using Satellite Remote-sensing Images in Kumamoto City, Japan -- 9: Validation of Hourly GSMaP and Ground Base Estimates of Precipitation for Flood Monitoring in Kumamoto, Japan -- 10: Appraisal of Surface and Groundwater of the Subarnarekha River Basin, Jharkhand, India: Using Remote Sensing, Irrigation Indices and Statistical Technique -- 11: Spatial and Temporal Variability of Sea Surface Height Anomaly and its Relationship with Satellite Derived Chlorophyll a Pigment Concentration in the Bay of Bengal -- 12: Monitoring Soil Moisture Deficit Using SMOS Satellite Soil Moisture: Correspondence through Rainfall-runoff Model -- Section IV: Artificial Intelligence and Hybrid Approaches -- 13: A Deterministic Model to Predict Frost Hazard in Agricultural Land Utilizing Remotely Sensed Imagery and GIS. , 14: A Statistical Approach for Catchment Calibration Data Selection in Flood Regionalisation -- 15: Prediction of Caspian Sea Level Fluctuations Using Artificial Intelligence -- 16: Spatio-temporal Uncertainty Model for Radar Rainfall -- 17: Soil Moisture Retrieval from Bistatic Scatterometer Measurements using Fuzzy Logic System -- Section V: Challenges in Geospatial Technology For Water Resources Development -- 18: Challenges in Geospatial Technology for Water Resources Development -- Index -- About the Editors.